A constraint-based approach to shape management in multimedia databases

Shape management is an important functionality in multimedia databases. Shape information can be used in both image acquisition and image retrieval. Several approaches have been proposed to deal with shape representation and matching. Among them, the data-driven approach supports searches for shapes based on indexing techniques. Unfortunately, efficient data-driven approaches are often defined only for specific types of shape. This is not sufficient in contexts in which arbitrary shapes should be represented. Constraint databases use mathematical theories to finitely represent infinite sets of relational tuples. They have been proved to be very useful in modeling spatial objects. In this paper, we apply constraint-based data models to the problem of shape management in multimedia databases. We first present the constraint model and some constraint languages. Then, we show how constraints can be used to model general shapes. The use of a constraint language as an internal specification and execution language for querying shapes is also discussed. Finally, we show how a constraint database system can be used to efficiently retrieve shapes, retaining the advantages of the already defined approaches.     

Applications of deformable models for in-dopth analysis and feature extraction from medical images—A review

Medical imaging plays important role for the practice of medicine globally and is rapidly increasing and getting sophisticated day by day. But accurate, fully automatic medical image analysis continues to be an elusive ideal for quantitative exploitation for diagnosis and therapy. The spatial relations between these anatomical structures as well as the biological shape variations are observed over a representative population of individuals. Deformable models (DMs) are being used in digital image analysis to maintain essential characteristics of image shape and intensity while accommodating fluctuations. Among model-based techniques, DMs offer a unique and powerful approach to image analysis that combines geometry, physics, and approximation theory. DMs are highly insightful interactive methods that allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task whenever necessary. The paper reviews the application of deformable models as a capable and robustly applied digital medical image analysis technique of the human body.     

Range query processing on single and multi GPU environments

Metric-space similarity search has proven suitable in a number of application domains such as multimedia retrieval and computational biology to name a few. These applications usually work on very large databases that are often indexed to speed-up on-line searches. To achieve efficient throughput, it is essential to exploit the intrinsic parallelism in the respective search query processing algorithms. Many strategies have been proposed in the literature to parallelize these algorithms either on shared or distributed memory multiprocessor systems. Lately, GPUs have been used to implement brute-force parallel search strategies instead of using index data structures. Indexing poses difficulties when it comes to achieve efficient exploitation of GPU resources. In this paper we propose single and multi GPU metric space techniques that efficiently exploit GPU tailored index data structures for parallel similarity search in large databases. The experimental results show that our proposal outperforms previous index-based sequential and OpenMP search strategies.     

Data-driven realizations of kernel and image representations and their application to fault detection and control system design

This paper deals with the data-driven design of observer-based fault detection and control systems. We first introduce the definitions of the data-driven forms of kernel and image representations. It is followed by the study of their identification. In the context of a fault-tolerant architecture, the design of observer-based fault detection, feed-forward and feedback control systems are addressed based on the data-driven realization of the kernel and image representations. Finally, the main results are demonstrated on the laboratory continuous stirred tank heater (CSTH) system.     

A real-time matching system for large fingerprint databases

With the current rapid growth in multimedia technology, there is an imminent need for efficient techniques to search and query large image databases. Because of their unique and peculiar needs, image databases cannot be treated in a similar fashion to other types of digital libraries. The contextual dependencies present in images, and the complex nature of two-dimensional image data make the representation issues more difficult for image databases. An invariant representation of an image is still an open research issue. For these reasons, it is difficult to find a universal content-based retrieval technique. Current approaches based on shape, texture, and color for indexing image databases have met with limited success. Further, these techniques have not been adequately tested in the presence of noise and distortions. A given application domain offers stronger constraints for improving the retrieval performance. Fingerprint databases are characterized by their large size as well as noisy and distorted query images. Distortions are very common in fingerprint images due to elasticity of the skin. In this paper, a method of indexing large fingerprint image databases is presented. The approach integrates a number of domain-specific high-level features such as pattern class and ridge density at higher levels of the search. At the lowest level, it incorporates elastic structural feature-based matching for indexing the database. With a multilevel indexing approach, we have been able to reduce the search space. The search engine has also been implemented on Splash 2-a field programmable gate array (FPGA)-based array processor to obtain near-ASIC level speed of matching. Our approach has been tested on a locally collected test data and on NIST-9, a large fingerprint database available in the public domain     

Optimizing memory bandwidth exploitation for OpenVX applications on embedded many-core accelerators

In recent years, image processing has been a key application area for mobile and embedded computing platforms. In this context, many-core accelerators are a viable solution to efficiently execute highly parallel kernels. However, architectural constraints impose hard limits on the main memory bandwidth, and push for software techniques which optimize the memory usage of complex multi-kernel applications. In this work, we propose a set of techniques, mainly based on graph analysis and image tiling, targeted to accelerate the execution of image processing applications expressed as standard OpenVX graphs on cluster-based many-core accelerators. We have developed a run-time framework which implements these techniques using a front-end compliant to the OpenVX standard, and based on an OpenCL extension that enables more explicit control and efficient reuse of on-chip memory and greatly reduces the recourse to off-chip memory for storing intermediate results. Experiments performed on the STHORM many-core accelerator demonstrate that our approach leads to massive reduction of time and bandwidth, even when the main memory bandwidth for the accelerator is severely constrained.     

Modeling and Estimation of Nonlinear Skin Mechanics for Animated Avatars

Data-driven models of human avatars have shown very accurate representations of static poses with soft-tissue deformations. However they are not yet capable of precisely representing very nonlinear deformations and highly dynamic effects. Nonlinear skin mechanics are essential for a realistic depiction of animated avatars interacting with the environment, but controlling physics-only solutions often results in a very complex parameterization task. In this work, we propose a hybrid model in which the soft-tissue deformation of animated avatars is built as a combination of a data-driven statistical model, which kinematically drives the animation, an FEM mechanical simulation. Our key contribution is the definition of deformation mechanics in a reference pose space by inverse skinning of the statistical model. This way, we retain as much as possible of the accurate static data-driven deformation and use a custom anisotropic nonlinear material to accurately represent skin dynamics. Model parameters including the heterogeneous distribution of skin thickness and material properties are automatically optimized from 4D captures of humans showing soft-tissue deformations.     

粗粒度可重构平台中循环自流水硬件实现

循环流水技术运用于粗粒度可重构体系结构可带来显著性能提升.循环控制、流水线同步和存储器有效利用是其中的关键问题.文中介绍了在粗粒度可重构体系结构LEAP上循环自主流水化的硬件实现.该方法基于支持循环迭代自动调度的控制部件、数据驱动ALU和可配置静态交换路由.利用动态调度循环中操作的优势,LEAP可发掘更高的程序并行度;分布式存储访问和高效数据重用则提高了带宽利用率.实验结果表明,相对于通用处理器,LEAP有13.08～535.65倍的性能提升.     

Tracking and Synthesizing Facial Motions with Dynamic Contours

Many researchers have studied techniques related to the analysis and synthesis of human heads under motion with face deformations. These techniques can be used for defining low-rate image compression algorithms (model-based image coding), cinema technologies, video-phones, as well as for applications of virtual reality, etc. Such techniques need a real-time performance and a strong integration between the mechanisms of motion estimation and those of rendering and animation of the 3D synthetic head/face. In this paper, a complete and integrated system for tracking and synthesizing facial motions in real-time with low-cost architectures is presented. Facial deformations curves represented as spatiotemporal B-splines are used for tracking in order to model the main facial features. In addition, the system proposed is capable of adapting a generic 3D wire-frame model of a head/face to the face that must be tracked; therefore, the simulations of the face deformations are produced by using a realistic patterned face.     

基于调用链控制流分析的大型微服务系统性能建模与异常定位

大型微服务系统中组件众多、依赖关系复杂, 由于故障传播的涟漪效应, 一个故障可能引起大规模服务异常, 快速识别异常并定位根因是服务质量保证的关键. 目前主要采用的调用链分析方法, 常常面临调用链结构复杂、实例数量庞大、存在大量小样本等问题, 因此提出基于调用链控制流分析, 将大量调用链结构聚合为少量方法调用模型; 并提...     

MEMORY ORGANIZATION AS THE MISSING LINK BETWEEN CASE-BASED REASONING AND INFORMATION RETRIEVAL IN BIOMEDICINE

Mémoire proposes a general framework for reasoning from cases in biology and medicine. Part of this project is to propose a memory organization capable of handling large cases and case bases as occur in biomedical domains. This article presents the essential principles for an efficient memory organization based on pertinent work in information retrieval (IR). IR systems have been able to scale up to terabytes of data taking advantage of large databases research to build Internet search engines. They search for pertinent documents to answer a query using term-based ranking and/or global ranking schemes. Similarly, case-based reasoning (CBR) systems search for pertinent cases using a scoring function for ranking the cases. Mémoire proposes a memory organization based on inverted indexes which may be powered by databases to search and rank efficiently through large case bases. It can be seen as a first step toward large-scale CBR systems, and in addition provides a framework for tight cooperation between CBR and IR.     

Deep Root Memory Optimized Indexing Methodology for Image Search Engines

Digitization has created an abundance of new information sources by altering how pictures are captured. Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing. This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets. Image retrieval usually encounters difficulties like a) merging the diverse representations of images and their Indexing, b) the low-level visual characters and semantic characters associated with an image are indirectly proportional, and c) noisy and less accurate extraction of image information (semantic and predicted attributes). This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively. Thus, retrieval becomes straightforward and rapid. This research also deals with deep root indexing with a multi-dimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost. We focus on the schema design on a non-clustered index solution, especially cover queries. This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing. Finally, we include non-key columns in addition to the key columns. Experiments on two image data sets ‘with and without’ filtered indexing show low query cost. We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing. The results show that retrieval by using deep root indexing is simple and fast.     

Multiple objects tracking in the presence of long-term occlusions

We present a robust object tracking algorithm that handles spatially extended and temporally long object occlusions. The proposed approach is based on the concept of “object permanence” which suggests that a totally occluded object will re-emerge near its occluder. The proposed method does not require prior training to account for differences in the shape, size, color or motion of the objects to be tracked. Instead, the method automatically and dynamically builds appropriate object representations that enable robust and effective tracking and occlusion reasoning. The proposed approach has been evaluated on several image sequences showing either complex object manipulation tasks or human activity in the context of surveillance applications. Experimental results demonstrate that the developed tracker is capable of handling several challenging situations, where the labels of objects are correctly identified and maintained over time, despite the complex interactions among the tracked objects that lead to several layers of occlusions.     

Low-cost image indexing for massive database

According to the trend in the modern society that utilizes various image related services and products, the amount of images created by diverse personal and industrial devices is immeasurably voluminous. The adoption of very uncommon or unique identifiers or index attributes with admissible storage requirement and adequate data representation enables massive image databases to process demanded operations effectively and efficiently. For the last decades, various content-based image retrieval techniques have been studied and contributed to support indexing in large digital image databases. However, the complexity, high processing cost of the content-based image retrieval techniques might create inefficiency regarding the configuration of a high-performance image database even though satisfying their own objectives. Moreover the indexing methods with the property of low cardinality might need additional indexing in order to provide strong uniqueness. In this paper, we present identifier generation methods for indexing which are efficient and effective in the perspective of cost and indexing performance as well. The proposed methods exploit the distribution of line segments and luminance areas in an image in order to compose identifiers with high cardinality. From the experimental evaluation, we’ve learned that our approaches are effective and efficient regarding processing time, storage requirement and indexing performance.     

Model-based invariants for 3-D vision

Invariance under a group of 3-D transformations seems a desirable component of an efficient 3-D shape representation. We propose representations which are invariant under weak perspective to either rigid or linear 3-D transformations, and we show how they can be computed efficiently from a sequence of images with a linear and incremental algorithm. We show simulated results with perspective projection and noise, and the results of model acquisition from a real sequence of images. The use of linear computation, together with the integration through time of invariant representations, offers improved robustness and stability. Using these invariant representations, we derive model-based projective invariant functions of general 3-D objects. We discuss the use of the model-based invariants with existing recognition strategies: alignment without transformation, and constant time indexing from 2-D images of general 3-D objects.     

Minimal Decomposition of Model-Based Invariants

Model-based invariants are relations between model parameters and image measurements, which are independent of the imaging parameters. Such relations are true for all images of the model. Here we describe an algorithm which, given L independent model-based polynomial invariants describing some shape, will provide a linear re-parameterization of the invariants. This re-parameterization has the properties that: (i) it includes the minimal number of terms, and (ii) the shape terms are the same in all the model-based invariants. This final representation has 2 main applications: (1) it gives new representations of shape in terms of hyperplanes, which are convenient for object recognition; (2) it allows the design of new linear shape from motion algorithms. In addition, we use this representation to identify object classes that have universal invariants.