Shape Matching and Object Recognition

We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Li, Fergus and Perona), a challenging dataset with large intraclass variation. Our approach yields a 45 % correct classification rate in addition to localization. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces

From Images to Shape Models for Object Detection

This research was supported by the EADS foundation, INRIA, CNRS, and SNSF. V. Ferrari was funded by a fellowship of the EADS foundation and by SNSF.International audienceWe present an object class detection approach which fully integrates the complementary strengths offered by shape matchers. Like an object detector, it can learn class models directly from images, and can localize novel instances in the presence of intra-class variations, clutter, and scale changes. Like a shape matcher, it finds the boundaries of objects, rather than just their bounding-boxes. This is achieved by a novel technique for learning a shape model of an object class given images of example instances. Furthermore, we also integrate Hough-style voting with a non-rigid point matching algorithm to localize the model in cluttered images. As demonstrated by an extensive evaluation, our method can localize object boundaries accurately and does not need segmented examples for training (only bounding-boxes)

The association of adverse life events and parental mental health with emotional and behavioral outcomes in young adults with autism spectrum disorder

People with autism spectrum disorder (ASD) are at increased risk of developing co-occurring mental health difficulties across the lifespan. Exposure to adverse life events and parental mental health difficulties are known risk factors for developing a range of mental health difficulties. This study investigates the association of adverse life events, parental stress and mental health with emotional and behavioral problems in young adults with ASD. One hundred and fifteen young adults with ASD derived from a population-based longitudinal study were assessed at three time-points (12-, 16-, and 23-year) on questionnaire measures of emotional and behavioral problems. Parent-reported exposure to adverse life events and parental stress/mental health were measured at age 23. We used structural equation modeling to investigate the stability of emotional and behavioral problems over time, and the association between adverse life events and parental stress and mental health and emotional and behavioral outcomes at 23-year. Our results indicate that exposure to adverse life events was significantly associated with increased emotional and behavioral problems in young adults with ASD, while controlling for symptoms in childhood and adolescence. Higher reported parental stress and mental health difficulties were associated with a higher frequency of behavioral, but not emotional problems, and did not mediate the impact of adverse life events. These results suggest that child and adolescent emotional and behavioral problems, exposure to life events and parent stress and mental health are independently associated, to differing degrees, with emotional or behavioral outcomes in early adulthood. Lay Summary: People with autism experience high rates of mental health difficulties throughout childhood and into adult life. Adverse life events and parental stress and mental health may contribute to poor mental health in adulthood. We used data at three time points (12-, 16-, and 23-year) to understand how these factors relate to symptoms at 23-year. We found that emotional and behavioral problems in childhood, adverse life events and parent mental health were all associated with increased emotional and behavioral problems in adulthood

Real-time parallel hashing on the gpu

Figure 1: Overview of our construction for a voxelized Lucy model, colored by mapping x, y, and z coordinates to red, green, and blue respectively (far left). The 3.5 million voxels (left) are input as 32-bit keys and placed into buckets of ≤ 512 items, averaging 409 each (center). Each bucket then builds a cuckoo hash with three sub-tables and stores them in a larger structure with 5 million entries (right). Close-ups follow the progress of a single bucket, showing the keys allocated to it (center; the bucket is linear and wraps around left to right) and each of its completed cuckoo sub-tables (right). Finding any key requires checking only three possible locations. We demonstrate an efficient data-parallel algorithm for building large hash tables of millions of elements in real-time. We consider two parallel algorithms for the construction: a classical sparse perfect hashing approach, and cuckoo hashing, which packs elements densely by allowing an element to be stored in one of multiple possible locations. Our construction is a hybrid approach that uses both algorithms. We measure the construction time, access time, and memory usage of our implementations and demonstrate real-time performance on large datasets: for 5 million key-value pairs, we construct a hash table in 35.7 ms using 1.42 times as much memory as the input data itself, and we can access all the elements in that hash table in 15.3 ms. For comparison, sorting the same data requires 36.6 ms, but accessing all the elements via binary search requires 79.5 ms. Furthermore, we show how our hashing methods can be applied to two graphics applications: 3D surface intersection for moving data and geometric hashing for image matching