Takeshi Mita

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Caltech-UCSD Birds 200 (CUB-200) is a challenging image dataset annotated with 200 bird species. It was created to enable the study of subordinate categorization, which is not possible with other popular datasets that focus on basic level categories (such as PASCAL VOC, Caltech-101, etc). The images were downloaded from the website Flickr and filtered by(More)
In this paper, we propose a new distinctive feature, called joint Haar-like feature, for detecting faces in images. This is based on co-occurrence of multiple Haar-like features. Feature co-occurrence, which captures the structural similarities within the face class, makes it possible to construct an effective classifier. The joint Haar-like feature can be(More)
This paper describes an object detection framework that learns the discriminative co-occurrence of multiple features. Feature co-occurrences are automatically found by sequential forward selection at each stage of the boosting process. The selected feature co-occurrences are capable of extracting structural similarities of target objects leading to better(More)
A structurally unique isoxazoline class compound, A1443, exhibits antiparasitic activity against cat fleas and dog ticks comparable to that of the commercial ectoparasiticide fipronil. This isoxazoline compound inhibits specific binding of the gamma-aminobutyric acid (GABA) receptor channel blocker [(3)H]4'-ethynyl-4-n-propylbicycloorthobenzoate (EBOB) to(More)
We present a robust frontal face detection method that enables the identification of face positions in images by combining the results of a low-resolution whole face and individual face parts classifiers. Our approach is to use face parts information and change the identification strategy based on the results from individual face parts classifiers. These(More)