Object Counting
Object Counting - Related Videos
10:19
Using a Hemacytometer to Count Cells
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2023
Many biomedical experiments require manipulation of a known quantity of cells, in order to achieve accurate, reproducible, and statistically-relevant data. Therefore, learning how to count cells is a particularly essential technique for any successful biomedical scientist. The most common way to count cells is by using a hemacytometer - an instrument that bears two laser-etched grids, which aid in the enumeration of an aliquot cells under a simple light microscope. This data can then be used to...
14:38
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Cited by 12 •
2012
We describe a novel methodology for creating naturalistic 3-D objects and object categories with precisely defined feature variations. We use simulations of the biological processes of morphogenesis and phylogenesis to create novel, naturalistic virtual 3-D objects and object categories that can then be rendered as visual images or haptic objects.
11:25
Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain
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Cited by 22 •
2009
The anatomical organization of the primate brain can provide important insights into normal and pathological conditions in humans. Unbiased stereology is a method for accurately and efficiently estimating the total neuron number (or other cell type) in a given reference space1.
05:47
Counting and Determining the Viability of Cultured Cells
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Cited by 19 •
2008
Determining the number of cells in culture is important in standardization of culture conditions and in performing accurate quantitation experiments. In this video, we demonstrate how cells are counted using a hemacytometer.
10:14
Object Substitution Masking
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University Visual masking is a term used by perceptual scientists to refer to a wide range of phenomena in which in an image is presented but not perceived by an observer because of the presentation of a second image. There are several different kinds of masking, many of them relatively intuitive and unsurprising. But one surprising and important type of masking is called Object Substitution Masking. It has been a focus of research in vision...
FAQs
Segmentation separates visible items from their surroundings, while tracking follows each item as its position changes across observations. Together, these processes help distinguish neighboring animals, social partners, food items, or arena objects and reduce the risk of counting one item repeatedly. This is especially important when behavioral data come from successive video frames rather than a single image.
The process links an identified item across changing positions instead of treating every appearance as a new object. Tracking therefore preserves the identity of an animal, partner, food item, or other target through successive observations. Preventing duplicate counts produces a more reliable quantitative record of how many distinct objects are present or involved in the behavior being measured.
Automated counting improves consistency across observations and makes it practical to analyze large video datasets. Manual review remains important because it can verify whether detection, segmentation, or tracking correctly identified the relevant objects. Combining automation with manual validation balances processing efficiency with quality control, supporting behavioral measurements that are more reproducible than relying on an unchecked counting process.
Counts can contribute to measurements of preference, exploration, interaction, and group behavior. For example, researchers may quantify animals near particular items, social partners present during an interaction, or food items associated with an experimental choice. The count supplies a numerical measure that can be compared across observations, while position changes and tracking add temporal and behavioral context.
A practical workflow begins by identifying the relevant items in an image, video frame, or observed arena. Researchers then use visual detection, segmentation, or tracking to distinguish objects and follow position changes when observations are repeated. Automated results can be checked manually before counts are used to quantify preference, exploration, interaction, or group behavior.
Object counting is useful when behavioral conclusions depend on how many animals, social partners, food items, or other objects are present or involved. In an experimental arena, the method can support comparisons of preference and exploration, document interactions, or quantify group behavior. It also provides a consistent approach for analyzing observations collected as images or video.
A single image provides an observation at one point, whereas successive video frames allow researchers to examine changes in object positions and maintain identities over time. Tracking those changes helps distinguish continued presence from a newly appearing item and supports analysis of interactions, exploration, and group behavior. Automated processing can extend this analysis across large video datasets, with manual validation.