Spatially varying image based lighting by light probe sequences

We present a novel technique for capturing spatially or temporally resolved light probe sequences, and using them for image based lighting. For this purpose we have designed and built a real-time light probe, a catadioptric imaging system that can capture the full dynamic range of the lighting incident at each point in space at video frame rates, while being moved through a scene. The real-time light probe uses a digital imaging system which we have programmed to capture high quality, photometrically accurate color images of 512×512 pixels with a dynamic range of 10000000:1 at 25 frames per second. By tracking the position and orientation of the light probe, it is possible to transform each light probe into a common frame of reference in world coordinates, and map each point and direction in space along the path of motion to a particular frame and pixel in the light probe sequence. We demonstrate our technique by rendering synthetic objects illuminated by complex real world lighting, first by using traditional image based lighting methods and temporally varying light probe illumination, and second an extension to handle spatially varying lighting conditions across large objects and object motion along an extended path.     

基于Linux平台的图像采集系统的设计与实现

为了在相关特殊领域能稳定可靠并且安全的对环境信息进行图像采集,研究设计了一种基于Linux系统平台的图像采集系统,通过对基于windows系统平台上的图像采集系统进行综合研究和分析,优化了Linux平台下图像采集系统的工作模型并且设计实现了系统的硬件平台和软件平台,实验结果表明,该系统可以安全可靠的控制全方位转动的摄像机对环境信息进行高清晰图像采集,有效的提高了图像采集速度和准确性.     

A modified Multi Scale Retinex algorithm with an improved global impressionof brightness for wide dynamic range pictures

This paper presents a new method of compressing the dynamic range of wide dynamic range scenes. This method is based on the Multi Scale Retinex algorithm. It improves the results of the original Multi Scale Retinex algorithm in a way that retains the global brightness contrast and the natural impression of the resulting image by recombining the original picture in a certain weight. Further improvement of the global brightness contrast is achieved by adjusting the histogram of the resulting picture. The paper explores the performance of this modified algorithm on different wide dynamic range scenes and points out its advantages over other dynamic range compression algorithms.Received: 7 November 2002, Accepted: 1 December 2003, Published online: 17 August 2004 Correspondence to: Orly Yadid-Pecht     

High dynamic range optimal fuzzy color image enhancement using Artificial Ant Colony System

This paper presents a novel approach for the enhancement of high dynamic range color images using fuzzy logic and modified Artificial Ant Colony System techniques. Two thresholds, the lower and the upper are defined to provide an estimate of the underexposed, mixed-exposed and overexposed regions in the image. The red, green and blue (RGB) color space is converted into Hue Saturation and Value (HSV) color space so as to preserve the chromatic information. Gaussian MFs suitable for the underexposed and overexposed regions of the image are used for the fuzzification. Parametric sigmoid functions are used for enhancing the luminance components of under and over-exposed regions. Mixed-exposed regions are left untouched throughout the process. An objective function comprising of Shannon entropy function as the information factor and visual appeal indicator is optimized using Artificial Ant Colony System to ascertain the parameters needed for the enhancement of a particular image. Visual appeal is preferred over the consideration of entropy so as to make the image human-eye-friendly. Separate power law operators are used for the saturation adjustment so as to restore the lost information. On comparison, this approach is found to be better than the bacterial foraging (BF)-based approach [1].     

Measure of image sharpness using eigenvalues

This paper deals with the design and implementation of a novel image sharpness metric based on the statistical approach. This sharpness metric is derived by modelling the image sharpness problem as a generalized eigenvalues problem. This problem is solved using Rayleigh quotient optimization where relevant statistical information of an image is extracted and then represented through a series of eigenvalues. The novelty of this paper comes from the application of eigenvalues in image sharpness metric formulation to provide robust assessment with the presence of various blur and noisy conditions. Firstly, the input image is normalized by its energy to minimize the effects caused by image contrast. Secondly, the covariance matrix is computed from this normalized image before it is diagonalized using Singular Values Decomposition (SVD) to obtain a series of eigenvalues. Finally, the image sharpness of the normalized image is determined by the trace of the first several eigenvalues. The performance of the proposed metric is gauged by comparing with several objective image sharpness metrics. Experimental results using synthetic and real images with known and unknown distortion conditions show the robustness and feasibility of the proposed metric in providing relative image sharpness. In particular, the proposed metric provides wider working range and more precise prediction consistency under all tested deformation conditions although it is slightly expensive in terms of computation than other metrics.     

基于PCI总线和DSP的实时图像采集与处理系统

以开发的实际系统为背景,论述了基于PCI总线和DSP的实时图像采集与处理系统的硬件及软件设计方案和实现方法。系统以数字CCD相机为图像采集设备,利用PCI总线的高速数据传输能力和DSP强大的数据处理能力,实现了图像的实时采集、处理和传输。     

Algebraic break of image ciphers based on discretized chaotic map lattices

In this paper, we provide an algebraic cryptanalysis of a recently proposed chaotic image cipher. We show that the secret parameters of the algorithm can be revealed using chosen-plaintext attacks. Our attack uses the orbit properties of the permutation maps to deduce encryption values for a single round. Once a single round encryption is revealed, the secret parameters are obtained using simple assignments.     

Efficient Multi‐image Correspondences for On‐line Light Field Video Processing

Light field videos express the entire visual information of an animated scene, but their shear size typically makes capture, processing and display an off‐line process, i. e., time between initial capture and final display is far from real‐time. In this paper we propose a solution for one of the key bottlenecks in such a processing pipeline, which is a reliable depth reconstruction possibly for many views. This is enabled by a novel correspondence algorithm converting the video streams from a sparse array of off‐the‐shelf cameras into an array of animated depth maps. The algorithm is based on a generalization of the classic multi‐resolution Lucas‐Kanade correspondence algorithm from a pair of images to an entire array. Special inter‐image confidence consolidation allows recovery from unreliable matching in some locations and some views. It can be implemented efficiently in massively parallel hardware, allowing for interactive computations. The resulting depth quality as well as the computation performance compares favorably to other state‐of‐the art light field‐to‐depth approaches, as well as stereo matching techniques. Another outcome of this work is a data set of light field videos that are captured with multiple variants of sparse camera arrays.     

Computer recognition of the human spinal outline using radiographic image processing

This paper deals with the determination of the outline of the spinal column on a P-A X-ray projection using methods of computerized image processing. The work is part of a larger project the goal of which is to model and quantify the three dimensional geometrical characteristics of normal and pathological spinal columns as well as to theoretically analyze the mechanics of the diseased spine. The data obtained by computer processing of both the P-A and lateral views would serve as input to the model. In order to delineate the edges of the spine, a technique of guided edge detection is incorporated into a successive approximation method. The plan for the guided edge detection is initially obtained using vertical signatures, while subsequent plans are found by fitting polynomials to the detected edges. Results are presented to demonstrate the ability of the algorithm to determine the desired spinal outlines.     

Watch to Edit: Video Retargeting using Gaze

We present a novel approach to optimally retarget videos for varied displays with differing aspect ratios by preserving salient scene content discovered via eye tracking. Our algorithm performs editing with cut, pan and zoom operations by optimizing the path of a cropping window within the original video while seeking to (i) preserve salient regions, and (ii) adhere to the principles of cinematography. Our approach is (a) content agnostic as the same methodology is employed to re‐edit a wide‐angle video recording or a close‐up movie sequence captured with a static or moving camera, and (b) independent of video length and can in principle re‐edit an entire movie in one shot. Our algorithm consists of two steps. The first step employs gaze transition cues to detect time stamps where new cuts are to be introduced in the original video via dynamic programming. A subsequent step optimizes the cropping window path (to create pan and zoom effects), while accounting for the original and new cuts. The cropping window path is designed to include maximum gaze information, and is composed of piecewise constant, linear and parabolic segments. It is obtained via L(1) regularized convex optimization which ensures a smooth viewing experience. We test our approach on a wide variety of videos and demonstrate significant improvement over the state‐of‐the‐art, both in terms of computational complexity and qualitative aspects. A study performed with 16 users confirms that our approach results in a superior viewing experience as compared to gaze driven re‐editing [ JSSH15 ] and letterboxing methods, especially for wide‐angle static camera recordings.