Finding any Waldo: zero-shot invariant and efficient visual search

Searching for a target object in a cluttered scene constitutes a fundamental challenge in daily vision. Visual search must be selective enough to discriminate the target from distractors, invariant to changes in the appearance of the target, efficient to avoid exhaustive exploration of the image, and must generalize to locate novel target objects with zero-shot training. Previous work has focused on searching for perfect matches of a target after extensive category-specific training. Here we show for the first time that humans can efficiently and invariantly search for natural objects in complex scenes. To gain insight into the mechanisms that guide visual search, we propose a biologically inspired computational model that can locate targets without exhaustive sampling and generalize to novel objects. The model provides an approximation to the mechanisms integrating bottom-up and top-down signals during search in natural scenes.Comment: Number of figures: 6 Number of supplementary figures: 1

Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search

This work proposes a process for efficiently searching over combinations of individual object 6D pose hypotheses in cluttered scenes, especially in cases involving occlusions and objects resting on each other. The initial set of candidate object poses is generated from state-of-the-art object detection and global point cloud registration techniques. The best-scored pose per object by using these techniques may not be accurate due to overlaps and occlusions. Nevertheless, experimental indications provided in this work show that object poses with lower ranks may be closer to the real poses than ones with high ranks according to registration techniques. This motivates a global optimization process for improving these poses by taking into account scene-level physical interactions between objects. It also implies that the Cartesian product of candidate poses for interacting objects must be searched so as to identify the best scene-level hypothesis. To perform the search efficiently, the candidate poses for each object are clustered so as to reduce their number but still keep a sufficient diversity. Then, searching over the combinations of candidate object poses is performed through a Monte Carlo Tree Search (MCTS) process that uses the similarity between the observed depth image of the scene and a rendering of the scene given the hypothesized pose as a score that guides the search procedure. MCTS handles in a principled way the tradeoff between fine-tuning the most promising poses and exploring new ones, by using the Upper Confidence Bound (UCB) technique. Experimental results indicate that this process is able to quickly identify in cluttered scenes physically-consistent object poses that are significantly closer to ground truth compared to poses found by point cloud registration methods.Comment: 8 pages, 4 figure

Optimized Ray Tracing

Táto práca sa zaoberá problematikou sledovania lúča a jej rôznymi optimalizáciami. Rozoberá matematický princíp sledovania lúča a hľadania priesečníka objektov scény s lúčom.  Tiež analyzuje rôzne osvetľovacie modely a efektívne budovanie kd-stromu pre rozdelenie objektov scény. Práca sa zameriava na návrh a implementáciu multiplatformnej a jednoducho rozšíriteľnej aplikácie vykonávajúcej ray-tracing virtuálnej scény v jazyku C++.This bachelor's thesis analyzes the problem of ray tracing and its optimizations. It describes mathematical principles of ray tracing and intersection searching of objects in scene and ray.  It also analyzes different shading models and efficient building of kd-tree for dividing scene objects. The thesis focuses on design and implementation of multiplatform and easily extensible ray tracing application in C++.

Remarks on searching zones of interest in locally uniform scenes

For searching for objects of limited size in a scene by using geometrical features, we suggest to segment not the whole scene, but only fragments of it containing the objects themselves and their environment. We examine the formalization of the search problem in the scene of such fragments, called the zones of interest, and discuss the results of its solution. © 2011 Pleiades Publishing, Ltd

Search of regions of interest in objects showing signs of a spot on locally homogeneous scenes

It is suggested that while searching a scene for objects of known sizes by means of spectral and geometrical signs, instead of segmentation of the entire scene, only those fragments that contain the objects themselves and their surroundings be segmented. The formalization of a search query in the scene of such fragments, called regions of interest, is discussed, methods of its solution are suggested, and the results obtained are examined further. © 2012 Pleiades Publishing, Ltd