An O(n) Time-Complexity Matrix Transpose on Torus Array Processor

This paper presents two advances towards the automated three-dimensional (3-D) analysis of thick and heavily-overlapped regions in cytological preparations such as cervical/vaginal smears. First, a high speed 3-D brightfield microscope has been developed, allowing the acquisition of image data at speeds approaching 30 optical slices per second. Second, algorithms have been developed to detect and segment nuclei in spite of the extremely high image variability and low contrast typical of such regions. The analysis of such regions is inherently a 3-D problem that cannot be solved reliably with conventional 2-D imaging and image analysis methods.High-Speed 3-D imaging of the specimen is accomplished by moving the specimen axially relative to the objective lens of a standard microscope (Zeiss) at a speed of 30 steps per second, where the stepsize is adjustable from 0.2 - 5μm. The specimen is mounted on a computer-controlled, piezoelectric microstage (Burleigh PZS-100, 68/μm displacement). At each step, an optical slice is acquired using a CCD camera (SONY XC-11/71 IP, Dalsa CA-D1-0256, and CA-D2-0512 have been used) connected to a 4-node array processor system based on the Intel i860 chip.

Download Full-text

New array processor architectures for two-dimensional FIR digital filters

IEE Proceedings E Computers and Digital Techniques ◽

10.1049/ip-e.1989.0032 ◽

1989 ◽

Vol 136 (4) ◽

pp. 234 ◽

Cited By ~ 1

Author(s):

M.M. Fahmy ◽

Y. Wan

Keyword(s):

Digital Filters ◽

Two Dimensional ◽

Array Processor ◽

Processor Architectures ◽

Fir Digital Filters

Download Full-text

Recognition of Birds in Blurred and Illumination Images by Local Features

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0128 ◽

2017 ◽

Vol 7 (7) ◽

pp. 243

Author(s):

Suresha .M ◽

. Sandeep

Keyword(s):

Computer Vision ◽

Time Complexity ◽

Feature Detection ◽

Feature Matching ◽

Fast Algorithms ◽

Local Features ◽

Experimental Demonstration ◽

Harris Corner ◽

Blurred Image ◽

Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

Download Full-text