Combined wavelet domain and motion compensated filtering compliant with video codecs

In this paper, we introduce the idea of using motion estimation resources from a video codec for video denoising. This is not straightforward because the motion estimators aimed for video compression and coding, tolerate errors in the estimated motion field and hence are not directly applicable to video denoising. To solve this problem, we propose a novel motion field filtering step that refines the accuracy of the motion estimates to a degree that is required for denoising. We illustrate the use of the proposed motion estimation method within a wavelet-based video denoising scheme. The resulting video denoising method is of low-complexity and receives comparable results with respect to the latest video denoising methods

DC-SIMD: dynamic communication for SIMD processors

SIMD (single instruction multiple data)-type processors have been found very efficient in image processing applications, because their repetitive structure is able to exploit the huge amount of data-level parallelism in pixel-type operations, operating at a relatively low energy consumption rate. However, current SIMD architectures lack support for dynamic communication between processing elements, which is needed to efficiently map a set of non-linear algorithms. An architecture for dynamic communication support has been proposed, but this architecture needs large amounts of buffering to function properly. In this paper, three architectures supporting dynamic communication without the need of large amounts of buffering are presented, requiring 98% less buffer space. Cycle-true communication architecture simulators have been developed to accurately predict the performance of the different architectures. Simulations with several test algorithms have shown a performance improvement of up to 5x compared to a locally connected SIMD-processor. Also, detailed area models have been developed, estimating the three proposed architectures to have an area overhead of 30-70% compared to a locally connected SIMD architecture (like the IMAP). When memory is taken into account as well, the overhead is estimated to be 13-28%

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but impeded by speed of computation. We have developed new ways to extract features based on notional use of physical paradigms, with parameterisation that is more familiar to a scientifically-trained user, aiming to make best use of computational resource. We describe how analogies based on gravitational force can be used for low-level analysis, whilst analogies of water flow and heat can be deployed to achieve high-level smooth shape detection. These new approaches to arbitrary shape extraction are compared with standard state-of-art approaches by curve evolution. There is no comparator operator to our use of gravitational force. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision

A SURVEY OF MULTISPECTRAL IMAGE DENOISING METHODS FOR SATELLITE IMAGERY APPLICATIONS

In comparison with the standard RGB or gray-scale images, the usual multispectral images (MSI) are intended to convey high definition and anauthentic representation for real world scenes to significantly enhance the performance measures of several other tasks involving with computervision, segmentation of image, object extraction, and object tagging operations. While procuring images form satellite, the MSI are often prone tonoises. Finding a good mathematical description of the learning-based denoising model is a difficult research question and many different researchesaccounted in the literature. Many have attempted its use with the application of neural network as a sparse learned dictionary of noisy patches.Furthermore, this approach allows several algorithm to optimize itself for the given task at hand using machine learning algorithm. However, inpractices, a MSI image is always prone to corruption by various sources of noises while procuring the images. In this survey, we studied the pasttechniques attempted for the noise influenced MSI images. The survey presents the outline of past techniques and their respective advantages incomparison with each other

A method for Measuring Contact Points in Human–Object Interaction Utilizing Infrared Cameras

This article presents a novel method for measuring contact points in human-object interaction. Research in multiple prehension-related fields, e.g., action planning, affordance, motor function, ergonomics, and robotic grasping, benefits from accurate and precise measurements of contact points between a subject's hands and objects. During interaction, the subject's hands occlude the contact points, which poses a major challenge for direct optical measurement methods. Our method solves the occlusion problem by exploiting thermal energy transfer from the subject's hand to the object surface during interaction. After the interaction, we measure the heat emitted by the object surface with four high-resolution infrared cameras surrounding the object. A computer-vision algorithm detects the areas in the infrared images where the subject's fingers have touched the object. A structured light 3D scanner produces a point cloud of the scene, which enables the localization of the object in relation to the infrared cameras. We then use the localization result to project the detected contact points from the infrared camera images to the surface of the 3D model of the object. Data collection with this method is fast, unobtrusive, contactless, markerless, and automated. The method enables accurate measurement of contact points in non-trivially complex objects. Furthermore, the method is extendable to measuring surface contact areas, or patches, instead of contact points. In this article, we present the method and sample grasp measurement results with publicly available objects.Peer reviewe

Extreme Two-View Geometry From Object Poses with Diffusion Models

Human has an incredible ability to effortlessly perceive the viewpoint difference between two images containing the same object, even when the viewpoint change is astonishingly vast with no co-visible regions in the images. This remarkable skill, however, has proven to be a challenge for existing camera pose estimation methods, which often fail when faced with large viewpoint differences due to the lack of overlapping local features for matching. In this paper, we aim to effectively harness the power of object priors to accurately determine two-view geometry in the face of extreme viewpoint changes. In our method, we first mathematically transform the relative camera pose estimation problem to an object pose estimation problem. Then, to estimate the object pose, we utilize the object priors learned from a diffusion model Zero123 to synthesize novel-view images of the object. The novel-view images are matched to determine the object pose and thus the two-view camera pose. In experiments, our method has demonstrated extraordinary robustness and resilience to large viewpoint changes, consistently estimating two-view poses with exceptional generalization ability across both synthetic and real-world datasets. Code will be available at https://github.com/scy639/Extreme-Two-View-Geometry-From-Object-Poses-with-Diffusion-Models

Fractal Geometry and Porosity

A fractal is an object or a structure that is self‐similar in all length scales. Fractal geometry is an excellent mathematical tool used in the study of irregular geometric objects. The concept of the fractal dimension, D, as a measure of complexity is defined. The concept of fractal geometry is closely linked to scale invariance, and it provides a framework for the analysis of natural phenomena in various scientific and engineering domains. The relevance of the power law scaling relationships is discussed. Fractal characteristics of porous media and the characteristic method of the porous media are also discussed. Different methods of analysis on the permeability of porous media are discussed in this chapter

Fuzzy logic-based approach to wavelet denoising of 3D images produced by time-of-flight cameras

In this paper we present a new denoising method for the depth images of a 3D imaging sensor, based on the time-of-flight principle. We propose novel ways to use luminance-like information produced by a time-of flight camera along with depth images. Firstly, we propose a wavelet-based method for estimating the noise level in depth images, using luminance information. The underlying idea is that luminance carries information about the power of the optical signal reflected from the scene and is hence related to the signal-to-noise ratio for every pixel within the depth image. In this way, we can efficiently solve the difficult problem of estimating the non-stationary noise within the depth images. Secondly, we use luminance information to better restore object boundaries masked with noise in the depth images. Information from luminance images is introduced into the estimation formula through the use of fuzzy membership functions. In particular, we take the correlation between the measured depth and luminance into account, and the fact that edges (object boundaries) present in the depth image are likely to occur in the luminance image as well. The results on real 3D images show a significant improvement over the state-of-the-art in the field. (C) 2010 Optical Society of Americ