Segment and Cut: A Simpler Method for Extracting Bounding-Boxes for Object Detection

Object detectors that are based on bounding-box regression are complex and require a lot of refinement to get good results. Is there a simpler way of doing object detection? To that end, I present a new method for object detection where you first segment an image, and then cut each object from the segmentation to produce its bounding box. The key to this method is that it uses only ground truth bounding box data to generate the ground truth segmentation mask, rather than using a semantic-segmentation mask which are pixel-perfect. Additionally, I present a modified Flood Fill algorithm for the cutting task. Using this method, I eliminate the need for bounding-box regression, anchor-boxes, region proposal methods and all of their associated complexities. Experiments show that my method gets competitive performance on the WIDER Face dataset with full-size images and runs between 30 and 50 FPS when using 640x480 pixel images.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Houston

Year dc:date.issued

2019

Author dc:creator

• Brown, Justin Michael 1994-

Advisor dc:contributor.advisor

• Shah, Shishir Kirit

Committee members dc:contributor.committeemember

• Wiley, Robert
• Kakadiaris, Ioannis A.

• Object detection
• Image Segmentation
• Face recognition

Statement dc:rights

• The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/10657/5755

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/5755