Recognition of common object-based categories found in toddler’s everyday object naming contexts

Previously, we investigated the distribution of instances of early-learned object-based categories in toddler’s realistic everyday learning episodes; we found important differences in terms of frequency and variability (3D vs. 2D; real object vs. realistic toy vs. simple shape). Using a picture book task we tested 24-36 month olds’ recognition of these categories in four conditions: Realistic; Features (only parts of the photo visible); Silhouettes; and Geons (a shape caricature version made with only 3-4 parts and no color or texture). Results show similar recognition for all Realistic and Silhouette versions; Geons were lower than the first two; and Features had the lowest recognition rate. Critically, categories with the highest variability in our previous study were readily recognized by Features but difficult to recognize in Geon version. These results suggest that abstracting global shape is influenced by the specific trajectory of experienced exemplars.Marie Curie International Incoming Fellowship PIIF-GA-2011-301155

Using 3D morphable models for face recognition in video

The 3D Morphable Face Model (3DMM) has been used for over a decade for creating 3D models from single images of faces. This model is based on a PCA model of the 3D shape and texture generated from a limited number of 3D scans. The goal of fitting a 3DMM to an image is to find the model coefficients, the lighting and other imaging variables from which we can remodel that image as accurately as possible. The model coefficients consist of texture and of shape descriptors, and can without further processing be used in verification and recognition experiments. Until now little research has been performed into the influence of the diverse parameters of the 3DMM on the recognition performance. In this paper we will introduce a Bayesian-based method for texture backmapping from multiple images. Using the information from multiple (non-frontal) views we construct a frontal view which can be used as input to 2D face recognition software. We also show how the number of triangles used in the fitting proces influences the recognition performance using the shape descriptors. The verification results of the 3DMM are compared to state-of-the-art 2D face recognition software on the MultiPIE dataset. The 2D FR software outperforms the Morphable Model, but the Morphable Model can be useful as a preprocesser to synthesize a frontal view from a non-frontal view and also combine images with multiple views to a single frontal view. We show results for this preprocessing technique by using an average face shape, a fitted face shape, with a MM texture, with the original texture and with a hybrid texture. The preprocessor has improved the verification results significantly on the dataset

Separate processing of texture and form in the ventral stream : evidence from fMRI and visual agnosia.

Real-life visual object recognition requires the processing of more than just geometric (shape, size, and orientation) properties. Surface properties such as color and texture are equally important, particularly for providing information about the material properties of objects. Recent neuroimaging research suggests that geometric and surface properties are dealt with separately, within the lateral occipital cortex (LOC) and the collateral sulcus (CoS), respectively. Here we compared objects that either differed in aspect ratio or in surface texture only, keeping all other visual properties constant. Results on brain-intact participants confirmed that surface texture activates an area in the posterior CoS, quite distinct from the area activated by shape within LOC. We also tested two patients with visual object agnosia, one of whom (DF) performed well on the texture task but at chance on the shape task, while the other (MS) showed the converse pattern. This behavioral double dissociation was matched by a parallel neuroimaging dissociation, with activation in CoS but not LOC in patient DF, and activation in LOC but not CoS in patient MS. These data provide presumptive evidence that the areas respectively activated by shape and texture play a causally necessary role in the perceptual discrimination of these features

On the Optimization of Visualizations of Complex Phenomena

The problem of perceptually optimizing complex visualizations is a difficult one, involving perceptual as well as aesthetic issues. In our experience, controlled experiments are quite limited in their ability to uncover interrelationships among visualization parameters, and thus may not be the most useful way to develop rules-of-thumb or theory to guide the production of high-quality visualizations. In this paper, we propose a new experimental approach to optimizing visualization quality that integrates some of the strong points of controlled experiments with methods more suited to investigating complex highly-coupled phenomena. We use human-in-the-loop experiments to search through visualization parameter space, generating large databases of rated visualization solutions. This is followed by data mining to extract results such as exemplar visualizations, guidelines for producing visualizations, and hypotheses about strategies leading to strong visualizations. The approach can easily address both perceptual and aesthetic concerns, and can handle complex parameter interactions. We suggest a genetic algorithm as a valuable way of guiding the human-in-the-loop search through visualization parameter space. We describe our methods for using clustering, histogramming, principal component analysis, and neural networks for data mining. The experimental approach is illustrated with a study of the problem of optimal texturing for viewing layered surfaces so that both surfaces are maximally observable

Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis

The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). Bilateral posterior-anterior knee radiographs were analyzed from the baseline of OAI and MOST datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. We used an oversegmentation strategy for partitioning knee images into the compact regions that follow natural texture boundaries. LBP, Fractal Dimension (FD), Haralick features, Shannon entropy, and HOG methods were computed within the standard ROI and within the proposed adaptive ROIs. Subsequently, we built logistic regression models to identify and compare the performances of each texture descriptor and each ROI placement method using 5-fold cross validation setting. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset.We used area under the receiver operating characteristic (ROC) curve (AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results. We found that the adaptive ROI improves the classification performance (OA vs. non-OA) over the commonly used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, LBP yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA

A Method for the Perceptual Optimization of Complex Visualizations

A common problem in visualization applications is the display of one surface overlying another. Unfortunately, it is extremely difficult to do this clearly and effectively. Stereoscopic viewing can help, but in order for us to be able to see both surfaces simultaneously, they must be textured, and the top surface must be made partially transparent. There is also abundant evidence that all textures are not equal in helping to reveal surface shape, but there are no general guidelines describing the best set of textures to be used in this way. What makes the problem difficult to perceptually optimize is that there are a great many variables involved. Both foreground and background textures must be specified in terms of their component colors, texture element shapes, distributions, and sizes. Also to be specified is the degree of transparency for the foreground texture components. Here we report on a novel approach to creating perceptually optimal solutions to complex visualization problems and we apply it to the overlapping surface problem as a test case. Our approach is a three-stage process. In the first stage we create a parameterized method for specifying a foreground and background pair of textures. In the second stage a genetic algorithm is applied to a population of texture pairs using subject judgments as a selection criterion. Over many trials effective texture pairs evolve. The third stage involves characterizing and generalizing the examples of effective textures. We detail this process and present some early results