In this paper, we propose an active contour model based on nonparametric independent and identically distributed (i.i.d.) statistics of the image that can segment an image without any a priori information about the intensity distributions of the region of interest or the background. This is not, however, the first active contour model proposed to solve the segmentation problem under these same assumptions. In contrast to prior active contour models based on nonparametric i.i.d. statistics, we do not formulate our optimization criterion according to any distance measure between estimated probability densities inside and outside the active contour. Instead, treating the segmentation problem as a pixel-wise classification problem, we formulate an active contour to minimize the unbiased pixel-wise average misclassification probability (AMP). This not only simplifies the problem by avoiding the need to arbitrarily select among many sensible distance measures to measure the difference between the probability densities estimated inside and outside the active contour, but it also solves a numerical conditioning problem that arises with such prior active contour models. As a result, the AMP model exhibits faster convergence with higher accuracy and robustness when compared to active contour models previously formulated to solve the same nonparametric i.i.d. statistical segmentation problem via probability distances. To discuss this improved numerical behavior more precisely, we introduce the notion of "conditioning ratio" and demonstrate that the proposed AMP active contour is numerically better conditioned (i.e., exhibits a much smaller conditioning ratio) than prior probability distance-based active contours.

Automotive surround view camera system is an emerging automotive ADAS (Advanced Driver Assistance System) technology that assists the driver in parking the vehicle safely by allowing him/her to see a top-down view of the 360 • surroundings of the vehicle. Such a system normally consists of four to six wide-angle (fish-eye lens) cameras mounted around the vehicle, each facing a different direction. From these camera inputs, a composite bird-eye view of the vehicle is synthesized and shown to the driver in real-time during parking. In this paper, we present a surround view camera solution that consists of three key algorithm components: geometric alignment, photometric alignment, and composite view synthesis. Our solution produces a seamlessly stitched bird-eye view of the vehicle from four cameras. It runs realtime on DSP C66x producing an 880 × 1080 output video at 30 fps.

Advance Driver Assistance Systems (ADAS), once limited to high end luxury automobiles are fast becoming popular with Mid and entry level segments driven in part by legislation coming in to effect in the latter part of this decade. These systems require support for a wide variety of applications, from surround-view visual systems to safety critical vision applications (eg Pedestrian Detect, automatic braking etc). In this white paper we describe some of the existing and emerging trends and applications in each of these segments along with the requirements and motivations for each of these features. We also highlight TI's automotive class TDA2x device, a state of the art automotive grade device capable of handling complex ADAS applications within a low power and cost budget.

We present an active geodesic contour model in which we constrain the evolving active contour to be a geodesic with respect to a weighted edge-based energy through its entire evolution rather than just at its final state (as in the traditional geodesic active contour models). Since the contour is always a geodesic throughout the evolution, we automatically get local optimality with respect to an edge fitting criterion. This enables us to construct a purely region-based energy minimization model without having to devise arbitrary weights in the combination of our energy function to balance edge-based terms with the region-based terms. We show that this novel approach of combining edge information as the geodesic constraint in optimizing a purely region-based energy yields a new class of active contours which exhibit both local and global behaviors that are naturally responsive to intuitive types of user interaction. We also show the relationship of this new class of globally constrained active contours with traditional minimal path methods, which seek global minimizers of purely edge-based energies without incorporating region-based criteria. Finally, we present some numerical examples to illustrate the benefits of this approach over traditional active contour models.

We propose a novel localized principal component analysis (PCA) based curve evolution approach which evolves the segmenting curve semi-locally within various target regions (divisions) in an image and then combines these locally accurate segmentation curves to obtain a global segmentation. The training data for our approach consists of training shapes and associated auxiliary (target) masks. The masks indicate the various regions of the shape exhibiting highly correlated variations locally which may be rather independent of the variations in the distant parts of the global shape. Thus, in a sense, we are clustering the variations exhibited in the training data set. We then use a parametric model to implicitly represent each localized segmentation curve as a combination of the local shape priors obtained by representing the training shapes and the masks as a collection of signed distance functions. We also propose a parametric model to combine the locally evolved segmentation curves into a single hybrid (global) segmentation. Finally, we combine the evolution of these semilocal and global parameters to minimize an objective energy function. The resulting algorithm thus provides a globally accurate solution, which retains the local variations in shape. We present some results to illustrate how our approach performs better than the traditional approach with fully global PCA.