In this paper, a robust background extraction and novel object detection are proposed, which comprise of filtering operations to detect non background objects in a monitoring scene. Conventionally, a statistical background model is extracted by using a training sequence without foreground objects and the background model parameters are being updated continuously to adapt changes in the scene. However, it is not possible to require a monitoring scene to be static. Furthermore, static objects in the scene could be adapted into the background. Problems arise when static objects start to move again. The convention method would produce false alarms in the detection process. In our proposed algorithm, two background models are constructed by using N-bins histogram method to indicate short term and long term changes of the monitoring scene. We then apply background subtractions to the current frame to obtain two error frames, which are combined for objects detection and classification. Extensive experimental work has been done, results of which show that the present approach provides a better solution compared with the conventional approach, including to resolve the problem of re-active objects.

A maximum descent (MD) method is proposed as an alternative to the generalized Lloyd (LBG) algorithm for vector quantization codebook generation. Compared with the LBG algorithm, the MD algorithm requires far less computation and generates codebooks that are superior in quality. This paper investigates and compares the performances of various partition techniques in the MD algorithm. In image vector quantization, the MD algorithm can produce codebooks with about 1 dB improvement in peak signal to noise ratio in less than 1/100 of the time required by the LBG algorithm.>

It is well-known that the exorbitant complexity of vector quantization's codebook design and encoding process have hindered the vector quantization based coding systems for real-time implementations. The-structured vector quantization (TSVQ) is one of the most efficient techniques to solve these problems. However, the encoder's memory requirement has increased as compared with the conventional full search vector quantization. To tackle this drawback and further reduce the searching complexity of TSVQ, new multisubspace tree-structured vector quantization design and encoding techniques are proposed in this paper. During the tree codebook generation process, each nonterminal node of the tree is associated with a partition (or region) and the child nodes are generated by further partitioning of these regions individually. Thus, different distortion measures can be used in different partitions for the node splitting. Based on this idea, the proposed multisubspace TSVQ design algorithms perform the vector quantization in spatial domain while using specially designed subspace distortions in transform domain as cost functions in the optimization process. Due to the energy compaction property of orthonormal transforms, dimensionality reduction is permitted by disregarding the components of minimal energy. The conventional Euclidean distortion is, therefore, adaptively replaced by dimension reduced transform domain subspace distortions based on the local statistics of the partition. Experimental results show that exceptionally low subspace dimension can be used in multisubspace TSVQ based on a fixed transform domain to obtain a similar performance as the conventional TSVQ or single subspace TSVQ. For example, a 256-level and 4x4 image multisubspace TSVQ using binary tree and 1-dimensional subspace distortions achieved almost 63 times computational reduction and 5 times storage reduction when compared with conventional full search vector quantization. In addition, the proposed generalized multisubspace TSVQ design algorithm is a general TSVQ design algorithm and which can also be utilized as a fast codebook design algorithm.

A technique for reducing the complexity of spatial-domain image vector quantization (VQ) is proposed. The conventional spatial domain distortion measure is replaced by a transform domain subspace distortion measure. Due to the energy compaction properties of image transforms, the dimensionality of the subspace distortion measure can be reduced drastically without significantly affecting the performance of the new quantizer. A modified LBG algorithm incorporating the new distortion measure is proposed. Unlike conventional transform domain VQ, the codevector dimension is not reduced and a better image quality is guaranteed. The performance and design considerations of a real-time image encoder using the techniques are investigated. Compared with spatial domain a speed up in both codebook design time and search time is obtained for mean residual VQ, and the size of fast RAM is reduced by a factor of four. Degradation of image quality is less than 0.4 dB in PSNR.

A new design and realization technique for noncausal digital filters with antisymmetrical impulse response is proposed. The resulting filter has purely imaginary frequency response suitable for realizing ideal Hilbert transformer and differentiator. It is shown that the antisymmetrical impulse response noncausal filter can be decomposed into two identical subfilters and two first order nonrecursive sections. The design requirements are translated to magnitude response specifications of the identical subfilters. The filter is then easily obtained using many well known design techniques based on magnitude response. A design example of Hilbert transformer is shown.