Image Processing
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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This is an accurately labeled dataset designed to support foreign bodies detection research in industrial production scenarios. In order to facilitate the reproduction of the results of the original paper, we provide this dataset for further research.
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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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Human intention is an internal, mental characterization for acquiring desired information. From
interactive interfaces, containing either textual or graphical information, intention to perceive desired
information is subjective and strongly connected with eye gaze. In this work, we determine such intention by
analyzing real-time eye gaze data with a low-cost regular webcam. We extracted unique features (e.g.,
Fixation Count, Eye Movement Ratio) from the eye gaze data of 31 participants to generate the dataset
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This dataset is composed by both real and sythetic images of power transmission lines, which can be fed to deep neural networks training and applied to line's inspection task. The images are divided into three distinct classes, representing power lines with different geometric properties. The real world acquired images were labeled as "circuito_real" (real circuit), while the synthetic ones were identified as "circuito_simples" (simple circuit) or "circuito_duplo" (double circuit). There are 348 total images for each class, 232 inteded for training and 116 aimed for validation/testing.
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this is a test
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