The LEDNet dataset consists of image data of a field area that are captured from a mobile phone camera.

Images in the dataset contain the information of an area where a PCB board is placed, containing 6 LEDs. Each state of the LEDs on the PCB board represents a binary number, with the ON state corresponding to binary 1 and the OFF state corresponding to binary 0. All the LEDs placed in sequence represent a binary sequence or encoding of an analog value.

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Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

Instructions: 

Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

4) Leaf Spot

5) Downy Mildew

The Quality parameters (Healthy/Diseased) and also classification based on the texture and color of leaves. For the object detection purpose researcher using an algorithm like Yolo,  TensorFlow, OpenCV, deep learning, CNN

I had collected a dataset from the region Amravati, Pune, Nagpur Maharashtra state the format of the images is in .jpg.

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The dataset is genrated by the fusion of three publicly available datasets: COVID-19 cxr image (https://github.com/ieee8023/covid-chestxray-dataset), Radiological Society of North America (RSNA) (https://www.kaggle.com/c/rsna-pneumonia-detection-challenge), and U.S.  national  library  of  medicine  (USNLM) collected  Montgomery  country - NLM(MC) (http

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1097 Views

Microscopic image based analysis plays an important role in histopathological computer based diagnostics. Identification of childhood medulloblastoma and its proper subtype from biopsy tissue specimen of childhood tumor is an integral part for prognosis.The dataset is of Childhood medulloblastoma (CMB) biopsy samples. The images are of 10x and 100x microscopic magnifications, uploaded in separate folders. The images consist of normal brain tissue cell samples and CMB cell samples of different WHO defined subtypes. An excel sheet is also uploaded for ease of data description.

Instructions: 

The dataset contains two folder of diffrent magnification images, i.e; 10x and 100x. The type of each image is described in the provided excel file. Each slide has a unique number and the number in bracket denotes that the corresponding image is of the single slide. 

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Raw data of image files that have been used to analyze the filling yields of the cavities in microscaffolds using murine and human induced stem cell-derived neurons, respectively. 

Instructions: 

The *.lif files can either be opened with the Laica LAS X software or Fiji using the bio-formats plugin.

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This dataset contains multispectral high resolution 1627 image patches of size 10 x 10 pixels with each pixel size of 10mx10m. These patches are generated from the Sentinel-2 (A/B) satellite images acquired during the period of October 2018 to May 2019. It covered one life cycle (12 months) of the sugarcane crop in the region of the Karnataka, India. Many parameters like plantation season, soil type, plantation type, crop variety and irrigation type that affects the growth of the sugarcane crop are considered while generating the samples.

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The rapid outbreak of COVID-19 due to the novel coronavirus SARS-COV-2 is the biggest issue faced by mankind today. It is important to detect the positive cases as early as possible to prevent the further spread of this pandemic.

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3344 Views

To improve reproductivity of our papar, we would upload experimental data and resources of evaluations.

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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on recognizing textures and materials in real-world images, which plays an important role in object recognition and scene understanding. Aiming at describing objects or scenes with more detailed information, we explore how to computationally characterize apparent or latent properties (e.g. surface smoothness) of materials, i.e., computational material characterization, which moves a step further beyond material recognition.

Instructions: 

Dataset Characteristics and Filename Formats

 

The "CoMMonS_FullResolution" folder includes 6912 full-resolution images (2560x1920). The "CoMMonS_Sampled" folder includes sampled images (resolution: 300x300), which are sampled from full-resolution images with different positions (x, y), rotation angles (r), zoom levels (z), a touching direction ("pile"), a lightness condition ("l5"), and a camera function setting ("ed3u"). This "CoMMonS_Sampled" folder is an example of a dataset subset for training and testing (e.g. 5: 1). Our dataset focuses on material characterization for one material (fabric) in terms of one of three properties (fiber length, smoothness, and toweling effect), facilitating a fine-grained texture classification. In this particular case, the dataset is used for a standard supervised problem of material quality evaluation. It takes fabric samples with human expert ratings as training inputs, and takes fabric samples without human subject ratings as testing inputs to predict quality ratings of the testing samples. The texture patches are classified into 4 classes according to each surface property measured by human sense of touch. For example, the human expert rates surface fiber length into 4 levels, from 1 (very short) to 4 (long), and similarly for smoothness and toweling effect. In short, the "CoMMonS_Sampled" folder includes 9 subfolders, each of which includes both sampled images and attribute class labels.

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382 Views

As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on recognizing textures and materials in real-world images, which plays an important role in object recognition and scene understanding. Aiming at describing objects or scenes with more detailed information, we explore how to computationally characterize apparent or latent properties (e.g. surface smoothness) of materials, i.e., computational material characterization, which moves a step further beyond material recognition.

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96 Views

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