Image Processing
Dataset for detecting faults during PCB manufacturing
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Today, the cameras are fixed everywhere, in streets, in vehicles, and in any public area. However, Analysis and extraction of information from images are required. Particularly, in autonomous vehicles and in smart applications that are developed to guide tourists. So, a large dataset of scene text images is an important and difficult factor in the extraction of textual information in natural images. It is the input to any computer vision system.
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Achieving digital intraoral impressions is a key step in orthodontic, implant, and repair. Compared with the complex and time-consuming traditional plaster impression method, the intraoral 3D scanner can obtain digital impressions in real time. However, because of the saliva, enamel, metallic denture, etc., the quality of the captured 2D image, which is used for feature measurement and 3D reconstruction, is usually degraded.
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There exist several commonly used datasets in relation to object detection that include COCO (with multiple versions) and ImageNet containing large annotations for 80 and 1000 objects (i.e. classes) respectively. However, very limited datasets are available comprising specific objects identified by visually imapeired people (VIP) such as wheel-bins, trash-Bags, e-Scooters, advertising boards, and bollard. Furthermore, the annotations for these objects are not available in existing sources.
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Images from a SCARA manipulator with co-planar colored markers moving in a plane.
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Sixteen omnidirectional images, taken from Salient360! [1] dataset, were used in the subjective test. The viewport videos are rendered using rectilinear projection. The pairwise comparison (PC) was selected as the subjective test method. More details on the viewport videos and the subject test procedures can be found in [2].
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The subjective assessment of the generalized perspective projection (parameterized with projection center d) was conducted using eight omnidirectional images, in equirectangular format, taken from the Salient360! dataset [1]. The Stimulus Comparison Adjectival Categorical Judgment (SCACJ) was selected as an evaluation method. More details about generalized perspective projection, rendered viewport images, and the subjective assessment procedure can be found in [2].
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This dataset is a collection of images and their respective labels containing multiple Indian coins of different denominations and their variations. The dataset only contains images of one side of each coin (Tail side) which contains the denomination value.
The samples were collected with the help of a mobile phone while the coins were placed on top of a white sheet of A4-sized paper.
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A crowdsourcing-based subjective evaluation of viewport images, rendered from ten omnidirectional images in equirectangular format (ERI), obtained with Pannini projection was conducted, aiming to assess the perceptual impact of the object shape deformation, introduced due to sphere-to-plane projections. More details on the Pannini projection, rendered viewports, and subjective test procedure can be found in [1].
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