Object detection and recognition are important problems in computer vision and pattern recognitio... more Object detection and recognition are important problems in computer vision and pattern recognition domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on computer based systems has proved to be a non-trivial task. In particular, despite significant research efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition systems in real time remain elusive. Here we present a survey of one particular approach that has proved very promising for invariant feature recognition and which is a key initial stage of multi-stage network architecture methods for the high level task of object recognition.
International Journal on Soft Computing, Artificial Intelligence and Applications, 2016
Object detection and recognition are important problems in computer vision and pattern recognitio... more Object detection and recognition are important problems in computer vision and pattern recognition domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on computer based systems has proved to be a non-trivial task. In particular, despite significant research efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition systems in real time remain elusive. Here we present a survey of one particular approach that has proved very promising for invariant feature recognition and which is a key initial stage of multi-stage network architecture methods for the high level task of object recognition.
International Journal of Computer Theory and Engineering, 2013
Multi-core processor technologies have become pervasive and mainstream. Several types of multi-co... more Multi-core processor technologies have become pervasive and mainstream. Several types of multi-core CPUs, including symmetric and asymmetric models, are emerging. A multi-core processor architecture may be defined as: on-chip clusters of heterogeneous functionality modules (processors), cooperating in the implementation of multiple concurrent applications. On these platforms, developing applications that truly take advantage of the power of multi-core capabilities is still a complex, error-prone, and challenging endeavor. Application code must be tuned to optimally fit the available resources. Operating System procedures must cover issues at lower abstraction layers, close to firmware, in order to enable features like optimal task/thread level scheduling depending upon the application requirements on the appropriate processor as per its characteristic. For Efficient scheduling of task or thread on multi core system the operating system scheduler must be aware about the underlying heterogeneity present in the system, also it must be aware about the characteristics of application (at static and at run time). Because as per the characteristic of the executing application the scheduler can take decision to schedule the task on available core so that optimal performance and good throughput can be achieved.
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 2016
Object detection and recognition are important problems in computer vision and pattern recognitio... more Object detection and recognition are important problems in computer vision and pattern recognition domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on computer based systems has proved to be a non-trivial task. In particular, despite significant research efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition systems in real time remain elusive. Here we present a survey of one particular approach that has proved very promising for invariant feature recognition and which is a key initial stage of multi-stage network architecture methods for the high level task of object recognition.
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 2016
Object detection and recognition are important problems in computer vision and pattern recognitio... more Object detection and recognition are important problems in computer vision and pattern recognition domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on computer based systems has proved to be a non-trivial task. In particular, despite significant research efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition systems in real time remain elusive. Here we present a survey of one particular approach that has proved very promising for invariant feature recognition and which is a key initial stage of multi-stage network architecture methods for the high level task of object recognition.
Object detection and recognition are important problems in computer vision and pattern recognitio... more Object detection and recognition are important problems in computer vision and pattern recognition domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on computer based systems has proved to be a non-trivial task. In particular, despite significant research efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition systems in real time remain elusive. Here we present a survey of one particular approach that has proved very promising for invariant feature recognition and which is a key initial stage of multi-stage network architecture methods for the high level task of object recognition.
International Journal on Soft Computing, Artificial Intelligence and Applications, 2016
Object detection and recognition are important problems in computer vision and pattern recognitio... more Object detection and recognition are important problems in computer vision and pattern recognition domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on computer based systems has proved to be a non-trivial task. In particular, despite significant research efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition systems in real time remain elusive. Here we present a survey of one particular approach that has proved very promising for invariant feature recognition and which is a key initial stage of multi-stage network architecture methods for the high level task of object recognition.
International Journal of Computer Theory and Engineering, 2013
Multi-core processor technologies have become pervasive and mainstream. Several types of multi-co... more Multi-core processor technologies have become pervasive and mainstream. Several types of multi-core CPUs, including symmetric and asymmetric models, are emerging. A multi-core processor architecture may be defined as: on-chip clusters of heterogeneous functionality modules (processors), cooperating in the implementation of multiple concurrent applications. On these platforms, developing applications that truly take advantage of the power of multi-core capabilities is still a complex, error-prone, and challenging endeavor. Application code must be tuned to optimally fit the available resources. Operating System procedures must cover issues at lower abstraction layers, close to firmware, in order to enable features like optimal task/thread level scheduling depending upon the application requirements on the appropriate processor as per its characteristic. For Efficient scheduling of task or thread on multi core system the operating system scheduler must be aware about the underlying heterogeneity present in the system, also it must be aware about the characteristics of application (at static and at run time). Because as per the characteristic of the executing application the scheduler can take decision to schedule the task on available core so that optimal performance and good throughput can be achieved.
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 2016
Object detection and recognition are important problems in computer vision and pattern recognitio... more Object detection and recognition are important problems in computer vision and pattern recognition domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on computer based systems has proved to be a non-trivial task. In particular, despite significant research efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition systems in real time remain elusive. Here we present a survey of one particular approach that has proved very promising for invariant feature recognition and which is a key initial stage of multi-stage network architecture methods for the high level task of object recognition.
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 2016
Object detection and recognition are important problems in computer vision and pattern recognitio... more Object detection and recognition are important problems in computer vision and pattern recognition domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on computer based systems has proved to be a non-trivial task. In particular, despite significant research efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition systems in real time remain elusive. Here we present a survey of one particular approach that has proved very promising for invariant feature recognition and which is a key initial stage of multi-stage network architecture methods for the high level task of object recognition.
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