擁有生物激勵神經(jīng)感知引擎的實時多目標(biāo)識別處理器-精選文檔

1、object recognitionfirst, various scale spaces are generated by a cascaded filtering for input video stream.then, key-points are extracted among neighbor scale spaces by local maxima/minima search, and each of them is converted to a descriptor vector that describes the magnitude and orientation of it

2、.last, the final recognition is made by nearest neighbor matching with pre-defined object database that generally includes over ten thousands of object descriptor vectors.disadvantage of simd processorstheir identical operations are not suitable for key-point or object level operations such as descr

3、iptor vector generation and database matching.the multi-core processor of exploits coarse-grained pes and memory-centric network-on-chip (noc) for task-level parallelism over data-level parallelism cannot provide enough computing power for real-time object recognition due to its data synchronization

4、 overhead.the papers worksection ii describes a visual perception based multi-object recognition algorithm in detail.section iii explains system architecture of the proposed processor.detailed designs of each building block are explained in section iv.section v describes the architecture. proposed n

5、oc communication the chip implementation and evaluation results follow in section vi.ii .visual perception based multi-object recognition a. visual perception based object recognition modelb. overall algorithm(注：(rois)regions-of-interest)iii. system architecture iv. building block designa. neural perception engineb. simd processor unitc. decision proces