We introduce BUDA.ART, a system designed to assist researchers in Art History, to explore and analyze an archive of pictures of Buddha statues. The system combines different CBIR and classical retrieval techniques to assemble 2D pictures, 3D statue scans and meta-data, that is focused on the Buddha facial characteristics. We build the system from an archive of 50,000 Buddhism pictures, identify unique Buddha statues, extract contextual information, and provide specific facial embedding to first index the archive. The system allows for mobile, on-site search, and to explore similarities of statues in the archive. In addition, we provide search visualization and 3D analysis of the statues.

This paper presents a technique for real-time texture-less object recognition and tracking on mobile devices. Our proposed algorithm is an even lighter-weight version of the recent state-of-the-art binary-based texture-less object detector BIND (Binary Integrated Net Descriptor), primarily customized for mobile device applications. This modification, termed BIND-Lite, employs various techniques to overcome the low-computational power of current mobile devices, while mostly retaining the texture-less object detection robustness of the original BIND. On current generation mobile devices, BIND-Lite was able to achieve runtime rates of up to 30 frames per second. To evaluate our algorithm, we have also designed a mobile augmented reality application coined IMPRINT, which renders logos/images onto detected objects to showcase BIND-Lite in a real-time mobile augmented reality setting.

This paper presents BIND (Binary Integrated Net Descriptor), a texture-less object detector that encodes multi-layered binary-represented nets for high precision edge-based description. Our proposed concept aligns layers of object-sized patches (nets) onto highly fragmented occlusion resistant line-segment midpoints (linelets) to encode regional information into efficient binary strings. These lightweight nets encourage discriminative object description through their high-spatial resolution, enabling highly precise encoding of the objects edges and internal texture-less information. BIND achieved various invariant properties such as rotation, scale and edge-polarity through its unique binary logical-operated encoding and matching techniques, while performing remarkably well in occlusion and clutter. Apart from yielding efficient computational performance, BIND also attained remarkable recognition rates surpassing recent state-of-the-art texture-less object detectors such as BORDER, BOLD and LINE2D.

This paper presents an algorithm coined BORDER (Bounding Oriented-Rectangle Descriptors for Enclosed Regions) for texture-less object recognition. By fusing a regional object encompassment concept with descriptor-based pipelines, we extend local-patches into scalable object-sized oriented rectangles for optimal object information encapsulation with minimal outliers. We correspondingly introduce a modified line-segment detection technique termed Linelets to stabilize keypoint repeatability in homogenous conditions. In addition, a unique sampling technique facilitates the incorporation of robust angle primitives to produce discriminative rotation-invariant descriptors. BORDER's high competence in object recognition particularly excels in homogenous conditions obtaining superior detection rates in the presence of high-clutter, occlusion and scale-rotation changes when compared with modern state-of-the-art texture-less object detectors such as BOLD and LINE2D on public texture-less object databases.