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Computer Vision & Image Processing

A vision-system result becomes comparable when it can be traced from the output, through its geometry or data structure and the operation that produced it, back to the source image and capture condition.

source framerepresentation layersobservable output

Scene

What enters the system

  • capture device
  • source scene
  • image sequence
  • sensor parameters

Representation

How the scene is encoded

  • pixels
  • regions
  • features
  • masks
  • scene graph

Computation

What changes the representation

  • model
  • pipeline
  • loss
  • matching
  • decoder

Evidence

What makes behavior observable

  • paired outputs
  • metrics
  • curves
  • UI states
ObjectStructureRecordForm
Input and output imagesProblem image, source image, processed resultAcquisition condition, annotation, paired outputImage series / comparison grid
Processing pipelineOrdered acts with branches and intermediate image objectsStep boundary, branch condition, object passed between stepsFlow / branch map
Model architectureEncoder, feature layers, heads, decoder or diffusion processLayer role, interface, intermediate representationLayered block diagram
TrainingDataset, feature extraction, objective, supervision, trained modelData construction, objective, parameter update, validationTraining / use pair
InferenceInput, model execution, threshold or matching, outputRuntime sequence, decision rule, post-processingInference flow
Vision data structuresMasks, trimaps, scene graphs, features, boxesFields, relationships, generation and consumptionSchema / object map
Interface statesCanvas, panels, controls, successive user actionsState transition, selected object, system responseUI state series
Detection geometryPredicted geometry, reference geometry, intersection, lossCoordinate convention, region construction, metricGeometry plate
Experimental evidenceInputs, comparator outputs, curves and metricsConfiguration, dataset, metric definition, resultResult grid / curve
Spatial and capture hardwareCamera, carrier, 2D–3D relation, depth or meshSensor placement, parameters, correspondenceDevice / spatial diagram

input → output

Visual pair

Processing result and the condition under which it was produced.

region / mask / mesh

Spatial object

Coordinate frame, membership rule, and relationship to the source image.

layers + objective

Model record

Data flow through the model and the operation performed at each boundary.

state / timeline

Runtime record

Trigger, transition, intermediate result, and visible system response.

Source

A prompt, reference image, class label, or control signal enters with a stated source and format.

Encode

A conditioning encoder produces a representation; its injection point and interface are identified.

Transform

The representation conditions an iterative denoising process in pixel or latent space.

Decode

A final representation is decoded or rescaled into an output image.

Compare

The output is tied to a test configuration, baseline, metric definition, and paired visual result.

The trace separates the public diffusion substrate from the particular conditioning, sampling, representation, or data-construction mechanism under review.

Training path ≠ inference pathMetric ≠ geometric constructionExample image ≠ mechanismFormula requires a visual anchor

What does an overlap or detection-loss value actually measure?

IoU and generalized IoU depend on four visible geometric objects: the predicted region, reference region, their intersection or union, and—when used—the smallest enclosing region.

  • coordinate convention
  • region-construction rule
  • formula-to-figure variable mapping

When does a mask, scene graph, feature map, or box become usable evidence?

The object needs defined fields or membership rules, the operation that creates it, and the downstream operation that consumes it. A free-standing visualization leaves the technical relation implicit.

  • object schema
  • producer
  • consumer
  • coordinate frame or value range

Where does the technical difference sit: data, training, model, or runtime?

Dataset construction, objective and parameter update belong to training; thresholds, matching, non-maximum suppression, decoding, and other output rules belong to inference. They are recorded on separate paths.

  • training-data construction
  • objective
  • updated parameter
  • runtime decision rule

The useful unit of comparison is a transformation at a defined representation boundary. A model name or output image alone does not locate the technical difference.

  1. Intermediate data objects expose what one operation produces and the next consumes.

  2. Training and inference contain different operations and require separate records.

  3. A metric is interpretable only when its geometry, variables, baseline, and test condition are fixed.

  • Source and capture conditions are comparable.
  • Training records are not substituted for runtime records.
  • The same metric definition and baseline are used across the comparison.