Analysis of a biologically-inspired system for real-time object recognition

We present a biologically-inspired system for real-time, feed-forward object recognition in cluttered scenes. Our system utilizes a vocabulary of very sparse features that are shared between and within different object models. To detect objects in a novel scene, these features are located in the image, and each detected feature votes for all objects that are consistent with its presence. Due to the sharing of features between object models our approach is more scalable to large object databases than traditional methods. To demonstrate the utility of this approach, we train our system to recognize any of 50 objects in everyday cluttered scenes with substantial occlusion. Without further optimization we also demonstrate near-perfect recognition on a standard 3-D recognition problem. Our system has an interpretation as a sparsely connected feed-forward neural network, making it a viable model for fast, feed-forward object recognition in the primate visual system

2.5D multi-view gait recognition based on point cloud registration

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM

Using Self-Contradiction to Learn Confidence Measures in Stereo Vision

Learned confidence measures gain increasing importance for outlier removal and quality improvement in stereo vision. However, acquiring the necessary training data is typically a tedious and time consuming task that involves manual interaction, active sensing devices and/or synthetic scenes. To overcome this problem, we propose a new, flexible, and scalable way for generating training data that only requires a set of stereo images as input. The key idea of our approach is to use different view points for reasoning about contradictions and consistencies between multiple depth maps generated with the same stereo algorithm. This enables us to generate a huge amount of training data in a fully automated manner. Among other experiments, we demonstrate the potential of our approach by boosting the performance of three learned confidence measures on the KITTI2012 dataset by simply training them on a vast amount of automatically generated training data rather than a limited amount of laser ground truth data.Comment: This paper was accepted to the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. The copyright was transfered to IEEE (https://www.ieee.org). The official version of the paper will be made available on IEEE Xplore (R) (http://ieeexplore.ieee.org). This version of the paper also contains the supplementary material, which will not appear IEEE Xplore (R

Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Data

Reliable human pose estimation (HPE) is essential to many clinical applications, such as surgical workflow analysis, radiation safety monitoring and human-robot cooperation. Proposed methods for the operating room (OR) rely either on foreground estimation using a multi-camera system, which is a challenge in real ORs due to color similarities and frequent illumination changes, or on wearable sensors or markers, which are invasive and therefore difficult to introduce in the room. Instead, we propose a novel approach based on Pictorial Structures (PS) and on RGB-D data, which can be easily deployed in real ORs. We extend the PS framework in two ways. First, we build robust and discriminative part detectors using both color and depth images. We also present a novel descriptor for depth images, called histogram of depth differences (HDD). Second, we extend PS to 3D by proposing 3D pairwise constraints and a new method that makes exact inference tractable. Our approach is evaluated for pose estimation and clinician detection on a challenging RGB-D dataset recorded in a busy operating room during live surgeries. We conduct series of experiments to study the different part detectors in conjunction with the various 2D or 3D pairwise constraints. Our comparisons demonstrate that 3D PS with RGB-D part detectors significantly improves the results in a visually challenging operating environment.Comment: The supplementary video is available at https://youtu.be/iabbGSqRSg

Optimization for automated assembly of puzzles

The puzzle assembly problem has many application areas such as restoration and reconstruction of archeological findings, repairing of broken objects, solving jigsaw type puzzles, molecular docking problem, etc. The puzzle pieces usually include not only geometrical shape information but also visual information such as texture, color, and continuity of lines. This paper presents a new approach to the puzzle assembly problem that is based on using textural features and geometrical constraints. The texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. Feature values are derived from these original and predicted images of pieces. An affinity measure of corresponding pieces is defined and alignment of the puzzle pieces is formulated as an optimization problem where the optimum assembly of the pieces is achieved by maximizing the total affinity measure. An fft based image registration technique is used to speed up the alignment of the pieces. Experimental results are presented on real and artificial data sets