4975119
Diffuse-Specular Separation and Depth Recovery from Image Sequences
Specular reflections present difficulties for many areas of computer vision such as stereo and segmentation. To separate specular and diffuse reflection components, previous approaches generally require accurate segmentation, regionally uniform reflectance or structured lighting. To overcome these limiting assumptions, we propose a method based on color analysis and multibaseline stereo that simultaneously estimates the separation and the true depth of specular reflections. First, pixels with a specular component are detected by a novel form of color histogram differencing that utilizes the epipolar constraint. This process uses relevant data from all the stereo images for robustness, and addresses the problem of color occlusions. Based on the Lambertian model of diffuse reflectance, stereo correspondence is then employed to compute for specular pixels their corresponding diffuse components in other views. The results of color-based detection aid the stereo correspondence, which determines both separation and true depth of specular pixels. Our approach integrates color analysis and multibaseline stereo in a synergistic manner to yield accurate separation and depth, as demonstrated by our results on synthetic and real image sequences.
3388099
Compressing and companding high dynamic range images with subband architectures
High dynamic range (HDR) imaging is an area of increasing importance, but most display devices still have limited dynamic range (LDR). Various techniques have been proposed for compressing the dynamic range while retaining important visual information. Multi-scale image processing techniques, which are widely used for many image processing tasks, have a reputation of causing halo artifacts when used for range compression. However, we demonstrate that they can work when properly implemented. We use a symmetrical analysis-synthesis filter bank, and apply local gain control to the subbands. We also show that the technique can be adapted for the related problem of "companding", in which an HDR image is converted to an LDR image, and later expanded back to high dynamic range.
2702895
Single-image reflectance estimation for relighting by iterative soft grouping
Reflectance values for image-based relighting are often estimated from grouped pixels with similar reflectance, but such groupings are difficult to compute with certainty for sparse image data. To address this problem, we propose an iterative method that aggregates BRDF data in a single image with known geometry and lighting by soft grouping, where pixels contribute to one another's estimate according to their degree of reflectance similarity. Estimation of specular reflectance is further improved by albedo-independent soft grouping of pixels based on shape continuity. With recovered reflectances, we demonstrate realistic relighting for synthetic and real scenes, including surfaces with spatially-varying reflectance.
8653140
Multibaseline stereo in the presence of specular reflections
We address the problem of accurate depth estimation using multibaseline stereo in the presence of specular reflections. Specular reflections can cause the intensity and color of corresponding points to change dramatically according to different viewpoints, thus producing severe matching errors for various stereo algorithms. In this paper we propose a new method to deal with this problem by treating specular reflections as occlusions. Our idea is to first detect specular pixels by computing the uncertainty of depth estimates. Then we combine the use of flexible windows and an adaptively selected subset of images to avoid these specular areas in all the multibaseline stereo images. Even though specularities may exist in the reference image, accurate depth is nevertheless estimated for all pixels. Experiments show that our consideration of specular reflections leads to improved stereo results.
149024
Removing photography artifacts using gradient projection and flash-exposure sampling
Flash images are known to suffer from several problems: saturation of nearby objects, poor illumination of distant objects, reflections of objects strongly lit by the flash and strong highlights due to the reflection of flash itself by glossy surfaces. We propose to use a flash and no-flash (ambient) image pair to produce better flash images. We present a novel gradient projection scheme based on a gradient coherence model that allows removal of reflections and highlights from flash images. We also present a brightness-ratio based algorithm that allows us to compensate for the falloff in the flash image brightness due to depth. In several practical scenarios, the quality of flash/no-flash images may be limited in terms of dynamic range. In such cases, we advocate using several images taken under different flash intensities and exposures. We analyze the flash intensity-exposure space and propose a method for adaptively sampling this space so as to minimize the number of captured images for any given scene. We present several experimental results that demonstrate the ability of our algorithms to produce improved flash images.
14760859
Multiple-cue illumination estimation in textured scenes
In this paper, we present a method that integrates cues from shading, shadow and specular reflections for estimating directional illumination in a textured scene. Texture poses a problem for lighting estimation, since texture edges can be mistaken for changes in illumination condition, and unknown variations in albedo make reflectance model fitting impractical. Unlike previous works which all assume known or uniform reflectance, our method can deal with the effects of textures by capitalizing on physical consistencies that exist among the lighting cues. Since scene textures do not exhibit such coherence, we use this property to minimize the influence of texture on illumination direction estimation. For the recovered light source directions, a technique for estimating their intensities in the presence of texture is also proposed.
46557593
A New Stochastic PSO Technique for Neural Network Training
Recently, Particle Swarm Optimization(PSO) has been widely applied for training neural network. To improve the performance of PSO for high-dimensional solution space which always occurs in training NN, this paper introduces a new paradigm of particle swarm optimization named stochastic PSO (S-PSO). The feature of the S-PSO is its high ability for exploration. Consequently, when swarm size is relatively small, S-PSO performs much better than traditional PSO in training of NN. Hence if S-PSO is used to realize training of NN, computational cost of training can be reduced significantly.
9099317
String extraction from color airline coupon image using statistical approach
A novel technique is presented in this paper to extract strings in color images of both business settlement plan (BSP) and non-BSP airline coupon. The essential concept is to remove non-text pixels from complex coupon images, rather than extract strings directly. First we transfer color images from RGB to HSV space, which is approximate uniformed, and then remove the black component of images using the property of HSV space. A statistical approach called principal components analysis (PCA) is applied to extract strings by removing the background decorative pattern based on priori environment. Finally, a method to validate and improve performance is present.
16012990
Parallel-array implementations of a non-restoring square root algorithm
In this paper we present a parallel-array implementation of a new non-restoring square root algorithm (PASQRT). The carry-save adder (CSA) is used in the parallel array. The PASQRT has several features unlike other implementations. First, it does not use redundant representation for square root result. Second, each iteration generates an exact resulting value. Next it does not require any conversion on the inputs of the CSA. And last, a precise remainder can be obtained immediately. Furthermore, we present an improved version-a root-select parallel-array implementation (RS-PASQRT) for fast result value generation. The RSPASQRT is capable of achieving up to about 150% speedup ratio over the PASQRT. The simplicity of the implementations indicates that the proposed approach is an alternative to consider when designing a fully pipelined square root unit.
13422174
Motion Planning of Multiple Agents in Virtual Environments on Parallel Architectures
We proposed in a previous paper (2006) a hybrid two-layered approach for motion planning of multiple agents in static virtual environments, consisting of open spaces connected by multiple narrow passages. The discrete generalized Voronoi diagram (GVD) of the environment is used to identify narrow passages, and plan the global path of each agent independently of other agents' global paths. As each agent moves along its global path, the agent's path is locally modified using the hybrid technique of combining steering behaviors with Coordination Graphs (CG), where coordination graphs are used for deadlock avoidance in the narrow passages. The planner in the previous paper was single threaded, and it was able to plan the motions of 30 agents moving around in a simple virtual environment with 3 narrow passages. If more agents are moving in a more complex virtual environment (i.e., with more narrow passages), we may not be able to construct and process all the coordination graphs in real-time. In this paper, we parallelize the single threaded planner in a supervisor-worker paradigm with Unix processes who communicate with each other using System V interprocess communication (IPC) mechanism. We show that significant, scalable speedups are obtained by constructing and processing coordination graphs in parallel on a symmetric multiprocessing (SMP) system.
10776344
Robust Head Tracking Based on a Multi-State Particle Filter
This paper proposes a novel method for robust and automatic realtime head tracking by fusing face and head cues within a multi-state particle filter. Due to large appearance variability of human head, most existing head tracking methods use little object-specific prior knowledge, resulting in limited discriminant power. In contrast, face is a distinct pattern much easier to capture, which motivates us to incorporate a vector-boosted multi-view face detector (C. Huang, et al., 2005) to lend strong aid to general head observation cues including color and contour edge. To simultaneously and collaboratively perform temporal inference of both the face state and the head state, a Markov-network-based particle filter is constructed using sequential belief propagation Monte Carlo (G. Hua, et al., 2004). Our approach is tested on sequences used by previous researchers as well as on new data sets which includes many challenging real-world cases, and shows robustness against various unfavorable conditions