Laboratorium voor Neuro-en Psychofysiologie, Faculteit der Geneeskunde, KU Leuven, Campus Gasthuisberg, Herestraat, B-3000 Leuven, Belgium and , 1 Department of Psychology and Neuroscience Program, University of Southern California, Hedco Neurosciences Building, MC 2520, Los Angeles, CA 90089-2520, USA
Rufin Vogels, Laboratorium voor Neuro-en Psychofysiologie, Faculteit der Geneeskunde, Campus Gasthuisberg, Herestraat, B-3000 Leuven, Belgium. Email: rufin.vogels{at}med.kuleuven.ac.be.
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Abstract |
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Introduction |
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Given the difficulty in computing an illumination-invariant object representation, it is important to determine how well primates are able to recognize objects under varying illumination conditions. Earlier workers (Tarr et al., 1998) found only small costs in a sequential samedifferent object-matching task when the illumination direction was varied between the first and second objects. However, these costs were present only for differences in cast shadows, but not for shading variations. More recently, it has been shown (Nederhouser et al., 2001
) that object recognition is even invariant for variations of the cast shadows when the position of the two objects is varied so that observers cannot use the absence of any display change (on same trials where the illumination direction is constant) as an artefactual cue to respond same. Tarr et al. also reported a small 19 ms reaction time (RT) cost (relative to a mean RT of 1358 ms) and no statistically significant effect on discrimination accuracy when naming previously learned objects at a novel illumination direction (Tarr et al., 1998
). Thus, overall, these psychophysical studies indicate that humans show considerable illumination invariance when classifying objects. The present study investigates whether the same invariance also holds at the single cell level in macaque monkeys.
Surprisingly little work has been done on the physiological basis of illumination-invariant recognition. Lesion work in the macaque monkey indicated that the inferior temporal (IT) cortex is critical for object recognition under varying conditions of illumination (Weiskrantz and Saunders, 1984). Hietanen and colleagues (Hietanen et al., 1992
) measured the effect of different lighting conditions of faces for face selective cells of the superior temporal sulcus (STS). However, considerable psycho-physical work indicates that, unlike object recognition, face recognition is impaired when the direction of illumination is varied (Johnston et al., 1992
; Braje et al., 1998
; Liu et al., 1999
). Hence, we measured the effect of shading variations, induced by varying the illumination direction, in images of non-face objects on the responses of IT neurons. The neurons were tested with images of different objects so that we could assess their object selectivity under different illumination directions. In addition to varying the illumination direction, we manipulated the illumination intensity. The latter affects both mean luminance and the contrast of edges inside and between the parts of an object. To determine whether the neurons responded only to the outer contour or were also sensitive to features inside the object, we also measured their responses to silhouettes of the objects.
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Materials and Methods |
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Two male rhesus monkeys served as subjects. These are two of the three animals used in previous work (Vogels et al., 2001). Before these experiments, a head post for head fixation and a scleral search coil were implanted under full anesthesia and strict aseptic conditions. After training in the fixation task, a stainless steel recording chamber was implanted stereotactically, guided by structural magnetic resonance imaging (MRI). The recording chambers were positioned dorsal to IT, allowing a vertical approach, as described previously (Janssen et al., 2000a
). A computerized tomography (CT) scan of the skull, with the guiding tube in situ, was obtained during the course of the recordings. Superposition of the coronal CT and MRI images, as well as depth readings of the white and grey matter transitions and of the skull basis during the recordings, allowed reconstruction of the recording positions before the animals were killed (Janssen et al., 2000a
). Histological confirmation of the recording sites of one animal is available (Vogels et al., 2001
). All surgical procedures and animal care were in accordance with the guidelines of NIH and of the KU Leuven Medical School.
Apparatus
The apparatus was identical to that described previously (Vogels et al., 2001). The animal was seated in a primate chair, facing a computer monitor (Phillips 21 in. display) on which the stimuli were displayed. The head of the animal was fixed and eye movements were recorded using the magnetic search coil technique. Stimulus presentation and the behavioral task were under the control of a computer, which also displayed and stored the eye movements. A Narishige microdrive, which was mounted firmly on the recording chamber, lowered a tungsten microelectrode (13 M
; Frederick Hair) through a guiding tube. The latter tube was guided using a Crist grid that was attached to the microdrive. The signals of the electrode were amplified and filtered, using standard single cell recording equipment. Single units were isolated on line using template matching software (SPS). The timing of the single units and the stimulus and behavioral events were stored with 1 ms resolution by a PC for later offline analysis. The PC also showed raster displays and histograms of the spikes and other events, that were sorted by stimulus.
Stimuli
The stimuli consisted of greylevel rendered images of 13 objects. These objects were the same as those used in previous human psychophysical (Biederman and Bar, 1999) and electrophysiological (Vogels et al., 2001
) studies. The objects were composed of two parts (geons) and were rendered on a white background (luminance = 56 cd/m2). The images (size ~6°; luminance gamma corrected) were shown at the center of the display. Four different kinds of images were used as stimuli.
High luminance, shaded objects
The 3D objects were rendered on a Silicon Graphics Indigo2 work station, using the Showcase Toolkit. Each object was illuminated by a high luminance light source positioned in front of the object, 45° below, 45° above, or 45° to the right or to the left of the object. The lighting model was based on OpenGL. The light source was a combination of three light sources: ambient, diffused and specular. We used a Gouraud shading model, where the values are calculated only at the vertices and then linearly interpolated for each surface. Since cast shadows were not rendered, the images of the same object differed only in their shading. The greylevels of the five images of the same object were adjusted so that these had the same mean luminance after gamma correction. The five different shading versions of each of the 13 objects are shown in Figure 1. Note that changes of the illumination direction produced strong variations in luminance and contrast of the surfaces of the same object. Also, both object parts were affected. The median of the mean luminances of the 13 high luminance, shaded objects was 32 cd/m2 (1st quartile = 29; 3rd quartile = 34).
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The same objects were rendered using each of the same five illumination directions as above, but with a lower luminance light source. This produced darker images of the same objects, as shown in Figure 2 for frontally illuminated versions of two objects. The median of the mean luminances of the 13 low luminance, shaded objects was 16 cd/m2 (1st quartile = 16; 3rd quartile = 19). A comparison of the responses to the high (a) and low (b) luminance shaded images allows an assessment of the effect of illumination intensity. The median change in luminance between the low and high luminance images was 48% (1st quartile = 44%; 3rd quartile = 48%).
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The silhouettes had the same outlines as the object images of the two preceding cases, but in this case no shading was present so the internal, feature information was lost. The greylevel of the pixels inside the object contours had a constant luminance value, equal to the mean luminance of the corresponding high luminance, shaded version of the object (gamma-corrected). Comparison of the responses to a silhouette and the shaded, high luminance versions of the same object allowed an assessment of the necessity of inner contours, features, etc. for responsivity and selectivity.
Black silhouettes
These were identical to those in the preceding section, except that the luminance of the silhouettes corresponded to the lowest greylevel (0.1 cd/m2). The contrast of borders of a shaded object may be much higher than the contrast of the border of a silhouette that has the same mean luminance as the shaded object. To control for this, we tested the responses to these black silhouettes of which the border had the highest possible contrast.
Task
Trials started with the onset of a small fixation target at the display's center on which the monkey was required to fixate. After a fixation period of 700 ms, the fixation target was replaced by the stimulus for 300 ms, followed by presentation of the fixation target for another 100 ms. If the monkey's gaze remained within a 1.5° fixation window until the end of the trial, it was rewarded with a drop of apple juice.
Test Protocols
Responsive neurons were searched by presenting a frontally illuminated version of the 13 objects, i.e. the original object images of Vogels et al. (Vogels et al., 2001). After isolating a neuron responsive to at least one of these 13 images, one or more of the following three tests were conducted.
Illumination Direction and Intensity (IDI) Test
Ten images of two objects were presented in an interleaved fashion. The 10 images were the high and low illumination, shaded versions of that object. The two objects were the object producing the largest in the search test (see above) and another one still producing a response, if possible. With this test, we assessed the influence of illumination direction and intensity on the response and selectivity of the neuron. The 20 stimulus conditions were presented in random order for at least 10 trials per stimulus.
Illumination direction and silhouette (IDS) test
Six images of four objects were presented in an interleaved fashion (usually 10 trials/stimulus). The five high luminance, shaded images and the silhouette of each of the four objects were shown. The four objects consisted of the objects producing the strongest response in the search test and three other ones. In general, the test included the two objects producing the strongest activity. In the initial version of this test, which was run on only four neurons, the silhouettes were not presented.
Silhouette Luminance Control (SLC) Test
This test consisted of three conditions: a shaded version of the object producing the largest response, its silhouette and its black silhouette. The three images were presented in interleaved fashion for at least 10 trials/stimulus each.
Each neuron was tested either with the IDI or with the IDS test. The SLC test was run after the IDS test in some neurons showing a difference between the response to the silhouette and the shaded images.
Data Analysis
For each trial, spikes were counted in windows of 300 ms duration. Net responses were computed by subtracting the baseline activity (i.e. the spike counts obtained in a window preceding stimulus onset) from the stimulus induced activity (i.e. the spike counts measured in a window starting 50 ms after stimulus onset). Statistical significance of responses was assessed by analysis of variance (ANOVA) which compared the spike counts in the two windows. All neurons reported in this paper showed statistically significant responses (ANOVA, P < 0.05). Other ANOVAs and post hoc comparisons were performed on the net responses to test for stimulus selectivity and differences between conditions. Additional parametric and/or non-parametric tests were used to assess the significance of difference between conditions or sensitivity indices. These will be described below.
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Results |
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Effect of Illumination Intensity
In 35 neurons, we compared the responses to the shaded objects under high and low illumination. Figure 3 shows the responses of two IT neurons in the different conditions of the IDI test. Open and closed symbols indicate the responses to the low and high illuminated objects, respectively. The neuron of Figure 3A
was shape selective, responding only to one of the two shapes tested (compare triangles and squares in Fig. 3A
). Its response to the best object was significantly modulated when changing the illumination intensity (ANOVA, main effect of illumination intensity: P < 0.008). Note that this effect of illumination intensity was inconsistent across the different illumination directions. Figure 3B
presents the responses of the IT neuron showing the strongest effect of illumination intensity that we encountered in this sample. It responded strongly to the high luminance, shaded images, but only weakly in the low luminance conditions (ANOVA, significant main effect of illumination intensity: P < 0.00001).
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Effects of Illumination Direction
Figure 5 shows the responses of two IT neurons to the five high luminance, shaded versions of two objects. The response of the neuron of Figure 5A
was not affected by illumination direction of its preferred object (ANOVA, P > 0.05), while the response of the neuron of Figure 5B
was strongly affected by the illumination direction (ANOVA, P < 0.00001). These two examples illustrate extremes of the effect of illumination direction on the responses of the IT neurons. The two neurons shown in Figure 3
provide additional examples in which the responses were affected significantly by shading variations.
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The large majority of the neurons responded differentially to the different objects. The responses of 86 (30/35) and 100% (59/59) of the neurons tested in the IDI and IDS tests were significantly modulated when varying the object (ANOVA, main effect of object: P < 0.05). Since the neurons were tested with four different objects in the IDS test, the results of that test were analyzed further. In order to quantify the degree of object selectivity in the IDS test in a similar way to that done for illumination intensity and illumination direction, we selected the best and worst responses for the above illumination direction condition. This particular direction was chosen because it turned out to have the largest mean image difference (i.e. the sum of the squared differences between greylevels of corresponding pixels in the two images) with the frontally illuminated object images. Since the objects for the IDS test were selected with frontally illuminated objects, responses to the above illuminated condition should not be affected much by this object selection, assuming there is a strong effect of shading. The shape modulation index was defined as the response difference between the best and worst of the four objects in the above illumination condition, divided by the mean standard deviation of these responses. The distribution of this shape modulation index, shown in Figure 6B, has a median value of 3.03 (n = 59, IDS test), which is about three times larger than those obtained for the magnitude and direction modulation indices this holds when the direction modulation (median = 1.03, n = 59) and shape modulation indices are compared for the same neurons in the same (IDS) test). Computation of physical image similarities (in pixel greylevels) showed that images of different objects are less similar than images of the same object but with different shading. Thus, although unlikely, it cannot be excluded that the greater modulation indices for objects compared to illumination direction merely reflect physical image similarity. Whichever the case, the large shape modulation indices clearly demonstrate that the more modest intensity and direction modulation indices do not result from an inherent limitation of the neurons to show greater, reliable response differences.
For 86% (51/59) of the neurons tested with four objects (IDS test), the responses to each of the five shaded versions of the best object were larger than the responses to the five images of the worst object. Examples of neurons showing this invariance of object preference to changes of illumination direction are shown in Figures 3, 5A, 7 and 10. This invariance is mainly present for the extremes of the object preference (best and worst object), since responses for images of the best object and for images of a less optimal object that is still driving the neuron can overlap (see Figs 3B, 5B, 7 and 8
).
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The responses of somewhat less than half of the neurons were significantly affected by the direction of illumination. Thus, it is possible that IT neurons not only signal object-attributes per se, but also code for illumination properties such as direction of illumination. The neuron of Figure 5B responded best to the same illumination direction for two objects, apparently suggesting a tuning for illumination direction. However, for other neurons the preferred illumination direction was usually not consistent over the objects tested. Of 35 neurons that showed an effect of illumination direction for two objects, only 31% (11/35) had the same preferred illumination direction of the two objects. This percentage does not differ significantly from the value of 20% expected by chance (binomial test, P > 0.05). Thus, IT neurons do not seem to code for direction of illumination independently of shape.
Given this absence of consistent coding for direction of illumination, what then is causing the modulations of IT responses by shading variations? Inspection of the images and corresponding neural responses strongly suggests that the neurons that show an effect of illumination direction were sensitive to the variation of particular features, such as the presence or absence of particular high-contrast planes or borders. For example, the neuron of which the responses to all 20 shaded images are shown in Figure 7, responds better to two brick objects when the luminance of their upper side is much lower than that of one of the other sides or the other curved part. A similar sensitivity for the contrast-dependent saliency of bordering planes likely underlies the shading effect for the neuron of Figure 5B
. Another example is shown in Figure 8
. This neuron generally responded best to a dark horizontal bar (cylinder or brick) partially overlapping a lighter plane. Note that the best responses are obtained when the bar is contrasted the most from the other part of the object, suggesting that variations in the degree of segmentation of overlapping shapes contribute to the effects of illumination direction.
Responses to Shaded Objects and their Silhouettes Compared
The neuron of Figure 8 responded weakly to the silhouettes. This agrees with the proposed effect of shading on object part segmentation (see above), since, in the silhouettes, the region where the two parts overlap cannot be segmented from the rest of the partially occluded object part. To determine the generality of this observation, we compared the responses to the shaded images and the silhouettes in the 55 neurons tested with the two sorts of stimuli. Figure 9
shows the average response to the five shaded images and the silhouettes, averaged for the best objects of all neurons tested. Overall, the responses to the silhouettes were significantly smaller than to each of the shaded versions (Scheffé post hoc tests: P < 0.05 for each of the five comparisons of shaded and silhouette images), while there was no significant difference in mean responses among the five shaded versions (Scheffé post hoc tests, not significant).
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Most of the neurons that showed no effect of shading and that responded differentially to the shaded objects versus their silhouettes, decreased their response to the silhouette. For each of the 17 neurons for which the response was affected by the silhouette manipulation, we normalized the response to each of the six images of the best object to the maximum of the response for these images. These normalized responses for each of the six images, averaged across the 17 neurons, are shown in Figure 11A. The figure shows that although these neurons were only very weakly, if at all, affected by illumination direction, their response decreased, on average, by ~50% when stimulated by the silhouette of the same object. In addition, it should be noted that 6 out the 17 neurons lost their shape selectivity (ANOVA, shape effect not significant: P > 0.05) when the four objects were presented as silhouettes. Figure 10B
presents an example of such a neuron: it responded much more strongly to one object compared to the three others when the objects were shaded, while the responses to the silhouettes of the same objects did not differ significantly.
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Discussion |
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Comparison with Previous Studies on Effects of Illumination
This is the first study of the effect of different object illumination conditions on the responses and/or selectivity of IT neurons, with the possible exception of one study in anesthetized monkeys that compared the responses to images having the opposite contrast polarity (Ito et al., 1994). However, the present results can be compared to those of previous studies using faces as images. It should be kept in mind that effects of lighting conditions on single neuronal responses may differ for objects and faces, since several psychophysical studies suggest a larger effect of illumination for face than for object recognition (Johnston et al., 1992
; Braje et al., 1998
; Liu et al., 1999
).
Rolls and Baylis (Rolls and Baylis, 1986) found a linear increase of the response of face-selective IT neurons with the logarithm of the contrast of faces. This agrees with the idea that the illumination effects found in our study reflect a sensitivity for the contrast of edges within or between parts of an object. Hietaenen et al. (Hietaenen et al., 1992) reported that 6 of 21 face selective neurons showed a response invariance to highly different and unusual illumination conditions. This proportion is smaller than what we found for images of objects in the present study. This quantitative difference between our results and theirs can be due to several factors, among which are: (i) the more unusual and extreme lighting conditions in the Hietanen et al. study compared to ours; (ii) the presence in the Hietanen et al. study of cast shadows, which were not present in our images; and (iii) a difference between face and object selectivity.
Effects of Illumination on Neural Responses to Objects
It is possible that under real viewing conditions, the invariance for shading variations is larger than found here. Indeed, due to limitations of the rendering software, some shading effects produce what appears to be differences in material rather than differences in shading of the same material. Thus, it is possible that some of the effects that we found reflect a sensitivity for material instead of for shading differences and that the cell may have correctly signaled objects with different materials. On the other hand, it should be pointed out that the present images did not contain cast shadows. These shadows might produce larger modulations than those we have observed for shading.
Neurons at earlier stages of the visual system are mainly sensitive to the stimulus contrast of image features (Kuffler, 1953). A sensitivity for the contrast of the internal features and/or outer borders of the object can explain the differential response of IT neurons to images of the same object but illuminated with different intensities. The latter sensitivity might also account for most of the illumination direction effects. Indeed, relating the images and the responses of neurons sensitive to illumination direction suggested that the saliency of edges between and within object parts can be critical for the modulation of the neuronal responses with illumination direction. Some directions of illumination may just improve or hinder the detection of particular features the neuron responds to and/or may enhance or decrease the segmentation of parts the neuron responds to from other, adjoining parts (Missal et al., 1997
). This suggestion on the origin of shading effects in IT differs from one in which object representations carry explicit information on lighting variables, such as direction of illumination. Earlier work (Tarr et al., 1998
) contains a discussion of the use of explicit representations of lighting parameters. In fact, we did not find a consistent coding of illumination direction.
A comparison of the responses to silhouettes and shaded images revealed that the response invariance for shading results from a sensitivity to the image borders in some, but not all, of the neurons. Indeed, in many neurons responses to the silhouettes differed from that to the shaded images, indicating that these neurons were also sensitive to features inside the object. However, this group of neurons tolerated quite large changes of the luminance gradients and discontinuities inside the object. How does this tolerance for shading variations arise? One possibility is that it results from the convergence of efferents of lower-order neurons, each responding to different shading-dependent features. This is analogous to a computational scheme used to explain both view- and illumination-independent object recognition (Riesenhuber and Poggio, 2000). A second, related, possibility is that these neurons respond to the 3D structure of the object (part). Indeed, shading is a potentially important cue for 3D-shape shape from shading (Horn, 1981
) and thus these neurons may respond to the 3D structure of the object as signaled by the shading variations. This is not unlikely given our previous finding of selectivity for stereo-defined 3D shape (Janssen et al., 2000a
, b
) in the rostral, lower bank of the STS, which is part of IT. However, further experiments in which the shading-defined depth-structure of an object is varied systematically are needed to probe this possibility.
Comparison with Computational Studies of Illumination-invariant Recognition
Computational work, discussed in the Introduction, shows that a single illumination invariant does not exist. However, edge or luminance gradient representations can go a long way for some types of objects (i.e. are quasi-invariants; see Introduction). If these sorts of representations were used at the level of IT, one may expect that the response modulations of the neurons would correlate with variations of these representations. It is clear that the latter is not the case for all neurons. For instance, the responses of the neuron shown in Figure 8 do not correlate with changes in an edge representation computed using the Canny Edge Detector (Fig. 12
) (Canny, 1986
), nor with changes in the distribution of the image gradients. Thus these neurons respond to feature variations that are not captured by these computational models. Of course, the biological validity of such computational models could be tested only in neurons of which the response does vary with illumination direction.
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Invariances of IT Responses and Invariant Object Recognition
Objects can be recognized despite changes in their retinal position (Biederman and Cooper, 1991) and size (Biederman and Cooper, 1992
). This position and size invariance has been linked to the position and size invariance of IT neuronal responses (Sato et al., 1980
; Schwartz et al., 1983
; Ito et al., 1995
; Logothetis et al., 1995
). However, the position and size invariance of IT neurons should be qualified. First, the response of many IT neurons depends on stimulus position and size (Ito et al., 1995
; Janssen et al., 2000b
;Op de Beeck and Vogels, 2000
) and thus IT responses per se are not that invariant to position and size changes. Second, the invariance holds, at least to some degree, for shape preferences. This invariance of shape preference is not absolute when considering other stimuli than the best and the worst shapes, since responses of individual neurons for optimal and less-optimal shapes may switch rank when changing position and size (Ito et al., 1995
). Given this, albeit not complete, invariance of shape preference, it has been suggested (Schwartz et al., 1983
; Vogels and Orban, 1996
) that it is not the absolute response, but the response relative to that of other shape selective neurons, that is critical for coding shape. Results on the shape cue invariance (Sary et al., 1993
; Tanaka et al., 2001
) and invariance of preference for partially occluded shapes (Kovacs et al., 1995
) of IT neurons fit this proposal.
The present results on invariance for illumination intensity and shading variations suggest that IT neurons that prefer a particular part or shape feature of an object, on average, still express that preference when the same object is viewed under different illumination conditions. Indeed, although the response can be affected by shading variations, the object preference is generally unaffected, an invariance that is similar to what is observed for changes of position and size. Thus, as may hold for size and position invariance, the relative population activity of neurons tuned to parts of different objects may underlie illumination-invariant recognition. In such a population code, both active and inactive neurons may play a critical role: cells tuned to parts other than those present in the presented object will remain inactive whichever illumination direction, while neurons tuned to the stimulus parts will be responsive, perhaps to different degrees that depend on the illumination conditions. Whether only real illumination-invariant neurons underlie invariant recognition or whether it is a more distributed code that includes illumination-variant neurons remains an open question.
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Acknowledgments |
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