We compare the performance of our SVRDM (support vector representation and discrimination machine, a new SVM classifier) to that of other popular classifiers on the moving and stationary target acquisition and recognition (MSTAR) synthetic aperture radar (SAR) database. We present new results for the 10-class MSTAR problem with confuser and clutter rejection. Much prior work on the 10-class database did not address confuser rejection. In our prior work [1], we presented our results on a benchmark three-class experiment with confusers to be rejected. In this paper, we extend results to the ten-class classification case with confuser and clutter rejection. Our SVRDM achieved perfect clutter rejection scores, but the clutter was not demanding. Energy-normalization, which was used in many prior algorithms, makes clutter chips similar to target chips and thus produces worse results. We do not energy-normalize data.
We consider rejection and classification tests on the MSTAR (moving and stationary target acquisition and recognition) public database. We follow a benchmark procedure, which involves classification of three object classes and rejection of two confusers. This problem is difficult, since MSTAR images are specular and each target has a full 360° aspect angle range. In addition, a classifier should be able to handle object variants and depression angle differences between the training and test sets. We employ a new support vector representation and discrimination machine (SVRDM) for its excellent rejection-classification capability. A new simple registration method is used. Test results are presented and compared with those of other algorithms. The proposed method was also applied to clutter rejection and produced perfect rejection scores.
We address rejection-classification problems, which have been ignored in most prior work. For such a system, a high classification rate and a low false alarm rate are simultaneously desired. We first propose a one-class support vector representation machine (SVRM). The SVRM achieves a high test set detection rate by requiring a high training set detection rate; the SVRM reduces the false alarm rate by minimizing the upper bound of the decision region. The SVRM is then extended to a new support vector representation and discrimination machine (SVRDM) classifier to address multiple-class cases. The theoretical basis for our new SVRDM as best at rejection of non-objects (imposters in face recognition) is provided, as are new σ parameter selection methods. Test results on face recognition and verification with both pose and illumination variations are presented.
Face recognition with both pose and illumination variations is considered. In addition, we consider the ability of the classifier to reject non-member or imposter face inputs; most prior work has not addressed this. Initially, non-registered test and training set imagery is used (this is realistic) and a two-step pose estimation/face recognition processor is employed which includes registration of the test input to the reference poses. A new SVRDM support vector representation and discrimination machine classifier is proposed and initial face recognition-rejection results are presented. Face recognition and verification results are presented.
Face recognition with both pose and illumination variations is considered. In addition, we consider the ability of the classifier to reject nonmember or imposter face inputs; most prior work has not addressed this. A new SVRDM (support-vector representation and discrimination machine) classifier is proposed, and initial face recognition-rejection results are presented.
Face recognition with both pose and illumination variations is considered. In addition, we consider the ability of the classifier to reject non-member or imposter face inputs; most prior work has not addressed this. A new SVRDM support vector representation and discrimination machine classifier is proposed and initial face recognition-rejection results are presented.
Face recognition with both pose and illumination variations is considered. In addition, we consider the ability of the classifier to reject non-member or imposter face inputs; most prior work has not addressed this. A new SVRDM support vector representation and discrimination machine classifier is proposed and initial face recognition-rejection results are presented.
A study was conducted on the use of morphological processing for detection in Uncooled Infra Red (UCIR) data using the Clutter type 1 and Clutter type 2 databases. The CMO algorithm was used with various modifications. A fixed minimum peak value was used rather than a fraction of the maximum peak per image. This is much more realistic, since many real scenes will not contain targets. One-dimensional directional structuring elements (SEs) were used for the Close Minus Open algorithm. We used the range of gray levels within the output blob peaks (blob analysis) and larger windows in peak sorting to reduce false alarms. A new dilation minus erosion morphological algorithm gave the best result.
Pattern recognition of ISAR small ship images is considered. These represent a formidable new distortion-invariant pattern recognition problem. Variations to the standard image formation steps were used. The preprocessing used is noted. These include new techniques to produce data with horizontal deckline and proper upward superstructure. New algorithms to determine if an input image is useful are developed; These were necessary for such data. Initial recognition results using new distortion-invariant filters are presented.
Pattern recognition of ISAR small ship images is considered. These represent a formidable new distortion-invariant pattern recognition problem. Variations to the standard image formation steps were used. The preprocessing used is briefly noted. These include new techniques to produce data with horizontal deckline and proper upward superstructure. New algorithms to determine if an input image is useful are developed. These were necessary for such data. New variations of standard distortion-invariant filters are developed to provide excellent recognition results.
Satellite data was regarded as a main information source by many key projects in forestry for the past 20 years. such as the comprehensive inventory of remote sensing in "Three north" protection forest, monitoring and assessing on forest fire, evaluation Yunnan provincial tropical forest in southwest China, assessing forest ecological effect, preventing desertification, monitoring insects and innovating on current forest resources survey system, etc. Under the support of computer techniques and models, a relationship was established between remote sensing data and forest environmental factors, so they can express and estimated with quantitative methods. The satellite data being used include TM.. SPOT NOAA ERS-1 and Radarsat. This paper aims to introduce the application ofsatellite remote sensing data in Chinese forestry, especially the methods of estimating for growing stock volume, appraising the efficiency of using remote sensing data, estimating forest fuels with remote sensing, discovering early and assessment on forest fire, as well as brief introduction of remote sensing application in ecological forestry forest environment and forestry division. All ofwork mentioned in this paper is special achievements of Chinese forestry experts.
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